{ "openapi": "3.0.3", "info": { "title": "Text Features", "version": "2.0", "description": "Your project description" }, "paths": { "/text/ai_detection/": { "post": { "operationId": "text_ai_detection_create", "description": "
Available Providers\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**sapling**|`v1`|5.0 (per 1000000 char)|1000 char\n|**winstonai**|`v2`|14.0 (per 1000000 char)|1 char\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Chinese**|`zh`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**French**|`fr`|\n|**German**|`de`|\n|**Spanish**|`es`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (China)**|`zh-CN`|\n\n
", "summary": "AI Content Detection", "tags": [ "Ai Detection" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textai_detectionAiDetectionRequest" }, "examples": { "RequestExample": { "value": { "providers": "sapling,winstonai", "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." }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textai_detectionResponseModel" }, "examples": { "ResponseExample": { "value": { "sapling": { "ai_score": 0.970070276931997, "items": [ { "text": "The panther, also known as the black panther, is a magnificent and enigmatic creature that captivates the imagination of many.", "prediction": "ai-generated", "ai_score": 0.9990817176020741, "ai_score_detail": 0.9990817176020741 }, { "text": "It is not a distinct species itself, but rather a melanistic variant of leopards and jaguars.", "prediction": "ai-generated", "ai_score": 0.7623477056593153, "ai_score_detail": 0.7623477056593153 }, { "text": "The mesmerizing black coat of the panther is a result of a genetic mutation that increases the production of dark pigment, melanin.", "prediction": "original", "ai_score": 0.012890975241315883, "ai_score_detail": 0.012890975241315883 }, { "text": "Panthers are highly adaptable predators, found primarily in dense forests and jungles across Africa, Asia, and the Americas.", "prediction": "ai-generated", "ai_score": 0.9241208913655489, "ai_score_detail": 0.9241208913655489 }, { "text": "Their stealthy nature and exceptional agility make them formidable hunters.", "prediction": "original", "ai_score": 3.0645117310434955e-07, "ai_score_detail": 3.0645117310434955e-07 }, { "text": "They are solitary creatures, preferring to roam alone in their vast territories, which can span over a hundred square miles.", "prediction": "ai-generated", "ai_score": 0.98354284228347, "ai_score_detail": 0.98354284228347 }, { "text": "Equipped with incredible strength and sharp retractable claws, panthers are skilled climbers and swimmers.", "prediction": "original", "ai_score": 0.2026922923822072, "ai_score_detail": 0.2026922923822072 }, { "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.", "prediction": "original", "ai_score": 0.2140626025123451, "ai_score_detail": 0.2140626025123451 }, { "text": "The diet of a panther consists mainly of deer, wild boar, and smaller mammals.", "prediction": "original", "ai_score": 0.37921425697733424, "ai_score_detail": 0.37921425697733424 }, { "text": "However, they are opportunistic hunters and can also target livestock and domestic animals in areas where their habitats overlap with human settlements.", "prediction": "ai-generated", "ai_score": 0.5961050762132343, "ai_score_detail": 0.5961050762132343 } ], "cost": 0.0 }, "winstonai": { "ai_score": 0.9999, "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.", "prediction": "ai-generated", "ai_score": 0.9968, "ai_score_detail": 0.9968 }, { "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.", "prediction": "ai-generated", "ai_score": 0.9997, "ai_score_detail": 0.9997 }, { "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.", "prediction": "ai-generated", "ai_score": 0.9998, "ai_score_detail": 0.9998 }, { "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.", "prediction": "ai-generated", "ai_score": 0.9994, "ai_score_detail": 0.9994 }, { "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.", "prediction": "ai-generated", "ai_score": 0.9999, "ai_score_detail": 0.9999 }, { "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.", "prediction": "ai-generated", "ai_score": 0.9999, "ai_score_detail": 0.9999 } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/anonymization/": { "post": { "operationId": "text_anonymization_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**openai**|-|`v3.0.0`|20.0 (per 1000000 token)|1 token\n|**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 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(per 1 token)|1 token\n|**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token\n|**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token\n|**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token\n|**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde\n|**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char\n|**microsoft**|-|`v3.1`|0.25 (per 1000000 char)|1000 char\n|**privateai**|-|`v3`|5.0 (per 1000000 char)|100 char\n|**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Afrikaans**|`af`|\n|**Arabic**|`ar`|\n|**Bambara**|`bm`|\n|**Belarusian**|`be`|\n|**Bengali**|`bn`|\n|**Bulgarian**|`bg`|\n|**Burmese**|`my`|\n|**Catalan**|`ca`|\n|**Chinese**|`zh`|\n|**Croatian**|`hr`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Estonian**|`et`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**Georgian**|`ka`|\n|**German**|`de`|\n|**Hebrew**|`he`|\n|**Hindi**|`hi`|\n|**Hungarian**|`hu`|\n|**Icelandic**|`is`|\n|**Indonesian**|`id`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Khmer**|`km`|\n|**Korean**|`ko`|\n|**Latvian**|`lv`|\n|**Lithuanian**|`lt`|\n|**Luxembourgish**|`lb`|\n|**Malay (macrolanguage)**|`ms`|\n|**Modern Greek (1453-)**|`el`|\n|**Norwegian**|`no`|\n|**Norwegian Bokmål**|`nb`|\n|**Panjabi**|`pa`|\n|**Persian**|`fa`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Romanian**|`ro`|\n|**Russian**|`ru`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Spanish**|`es`|\n|**Swahili (macrolanguage)**|`sw`|\n|**Swedish**|`sv`|\n|**Tagalog**|`tl`|\n|**Tamil**|`ta`|\n|**Thai**|`th`|\n|**Turkish**|`tr`|\n|**Ukrainian**|`uk`|\n|**Vietnamese**|`vi`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (Simplified)**|`zh-Hans`|\n|**Chinese (Traditional)**|`zh-Hant`|\n|**Portuguese (Brazil)**|`pt-BR`|\n|**Portuguese (Portugal)**|`pt-PT`|\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n|**xai**|`grok-2-latest`|\n\n
", "summary": "Anonymization", "tags": [ "Anonymization" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/texttopic_extractiontextanonymizationtextmoderationtextnamed_entity_recognitiontextkeyword_extractiontextsyntax_analysistextsentiment_analysisTextAnalysisRequest" }, "examples": { "RequestExample": { "value": { "providers": "xai,privateai,amazon,openai,microsoft", "language": "en", "text": "Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed. First is the product reviews and pricing. There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product. There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc). The second issue is they make it too difficult to get help when there's an issue with an order." }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textanonymizationResponseModel" }, "examples": { "ResponseExample": { "value": { "xai": { "result": "The phone number of *** is the **************.", "entities": [ { "offset": 20, "length": 3, "category": "PersonalInformation", "subcategory": "Name", "original_label": "name", "content": "Luc", "confidence_score": 0.9 }, { "offset": 31, "length": 14, "category": "PersonalInformation", "subcategory": "Phone", "original_label": "phonenumber", "content": "06 21 32 43 54", "confidence_score": 0.95 } ], "cost": 0.0 }, "privateai": { "result": "The phone number of *** is the **************.", "entities": [ { "offset": 20, "length": 3, "category": "PersonalInformation", "subcategory": "Name", "original_label": "NAME_GIVEN", "content": "Luc", "confidence_score": 0.912 }, { "offset": 31, "length": 14, "category": "PersonalInformation", "subcategory": "Phone", "original_label": "PHONE_NUMBER", "content": "06 21 32 43 54", "confidence_score": 0.904 } ], "cost": 0.0 }, "amazon": { "result": "The phone number of *** is the **************.", "entities": [ { "offset": 20, "length": 3, "category": "PersonalInformation", "subcategory": "Name", "original_label": "NAME", "content": "Luc", "confidence_score": 1.0 }, { "offset": 31, "length": 14, "category": "DateAndTime", "subcategory": "DateTime", "original_label": "DATE_TIME", "content": "06 21 32 43 54", "confidence_score": 0.877 } ], "cost": 0.0 }, "openai": { "result": "The phone number of *** is the ** ** ** ** **.", "entities": [ { "offset": 20, "length": 3, "category": "PersonalInformation", "subcategory": "Name", "original_label": "NAME", "content": "Luc", "confidence_score": 1.0 }, { "offset": 31, "length": 14, "category": "PersonalInformation", "subcategory": "Phone", "original_label": "PHONE_NUMBER", "content": "06 21 32 43 54", "confidence_score": 1.0 } ], "usage": { "completion_tokens": 125, "prompt_tokens": 946, "total_tokens": 1071, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0 }, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 } }, "cost": 0.0 }, "microsoft": { "result": "The phone number of *** is the **************.", "entities": [ { "offset": 20, "length": 3, "category": "PersonalInformation", "subcategory": "Name", "original_label": "Person", "content": "Luc", "confidence_score": 0.99 }, { "offset": 31, "length": 14, "category": "PersonalInformation", "subcategory": "Phone", "original_label": "PhoneNumber", "content": "06 21 32 43 54", "confidence_score": 0.8 } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/chat/": { "post": { "operationId": "text_chat_create", "description": "
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token\n|**meta**|**llama3-1-70b-instruct-v1:0**|`boto3 (v1.35.84)`|0.72 (per 1000000 token)|1 token\n|**meta**|**llama3-1-8b-instruct-v1:0**|`boto3 (v1.35.84)`|0.22 (per 1000000 token)|1 token\n|**meta**|-|`boto3 (v1.35.84)`|0.15 (per 1000000 token)|1 token\n|**mistral**|-|`v0.0.1`|6.0 (per 1000000 token)|1 token\n|**mistral**|**pixtral-large-latest**|`v0.0.1`|6e-06 (per 1 token)|1 token\n|**mistral**|**mistral-saba-latest**|`v0.0.1`|0.6 (per 1000000 token)|1 token\n|**mistral**|**mistral-small-latest**|`v0.0.1`|3e-07 (per 1 token)|1 token\n|**mistral**|**mistral-large-latest**|`v0.0.1`|6e-06 (per 1 token)|1 token\n|**mistral**|**codestral-latest**|`v0.0.1`|3e-06 (per 1 token)|1 token\n|**mistral**|**codestral-2405**|`v0.0.1`|3e-06 (per 1 token)|1 token\n|**mistral**|**codestral-2508**|`v0.0.1`|9e-07 (per 1 token)|1 token\n|**mistral**|**codestral-mamba-latest**|`v0.0.1`|2.5e-07 (per 1 token)|1 token\n|**mistral**|**devstral-medium-2507**|`v0.0.1`|2e-06 (per 1 token)|1 token\n|**mistral**|**devstral-small-2505**|`v0.0.1`|3e-07 (per 1 token)|1 token\n|**mistral**|**devstral-small-2507**|`v0.0.1`|3e-07 (per 1 token)|1 token\n|**mistral**|**labs-devstral-small-2512**|`v0.0.1`|3e-07 (per 1 token)|1 token\n|**mistral**|**devstral-2512**|`v0.0.1`|2e-06 (per 1 token)|1 token\n|**mistral**|**magistral-medium-2506**|`v0.0.1`|5e-06 (per 1 token)|1 token\n|**mistral**|**magistral-medium-2509**|`v0.0.1`|5e-06 (per 1 token)|1 token\n|**mistral**|**magistral-medium-latest**|`v0.0.1`|5e-06 (per 1 token)|1 token\n|**mistral**|**magistral-small-2506**|`v0.0.1`|1.5e-06 (per 1 token)|1 token\n|**mistral**|**magistral-small-latest**|`v0.0.1`|1.5e-06 (per 1 token)|1 token\n|**mistral**|**mistral-large-2402**|`v0.0.1`|1.2e-05 (per 1 token)|1 token\n|**mistral**|**mistral-large-2407**|`v0.0.1`|9e-06 (per 1 token)|1 token\n|**mistral**|**mistral-large-2411**|`v0.0.1`|6e-06 (per 1 token)|1 token\n|**mistral**|**mistral-large-3**|`v0.0.1`|1.5e-06 (per 1 token)|1 token\n|**mistral**|**mistral-medium**|`v0.0.1`|8.1e-06 (per 1 token)|1 token\n|**mistral**|**mistral-medium-2312**|`v0.0.1`|8.1e-06 (per 1 token)|1 token\n|**mistral**|**mistral-medium-2505**|`v0.0.1`|2e-06 (per 1 token)|1 token\n|**mistral**|**mistral-medium-latest**|`v0.0.1`|2e-06 (per 1 token)|1 token\n|**mistral**|**mistral-small**|`v0.0.1`|3e-07 (per 1 token)|1 token\n|**mistral**|**mistral-tiny**|`v0.0.1`|2.5e-07 (per 1 token)|1 token\n|**mistral**|**open-codestral-mamba**|`v0.0.1`|2.5e-07 (per 1 token)|1 token\n|**mistral**|**open-mistral-7b**|`v0.0.1`|2.5e-07 (per 1 token)|1 token\n|**mistral**|**open-mistral-nemo**|`v0.0.1`|3e-07 (per 1 token)|1 token\n|**mistral**|**open-mistral-nemo-2407**|`v0.0.1`|3e-07 (per 1 token)|1 token\n|**mistral**|**open-mixtral-8x22b**|`v0.0.1`|6e-06 (per 1 token)|1 token\n|**mistral**|**open-mixtral-8x7b**|`v0.0.1`|7e-07 (per 1 token)|1 token\n|**mistral**|**pixtral-12b-2409**|`v0.0.1`|1.5e-07 (per 1 token)|1 token\n|**mistral**|**pixtral-large-2411**|`v0.0.1`|6e-06 (per 1 token)|1 token\n|**perplexityai**|-|`v1.0`|2.0 (per 1000000 token)|1 token\n|**perplexityai**|**sonar-pro**|`v1.0`|30.0 (per 1000000 token)|1 token\n|**perplexityai**|**sonar**|`v1.0`|2.0 (per 1000000 token)|1 token\n|**anthropic**|-|`bedrock-2023-05-31`|15.0 (per 1000000 token)|1 token\n|**anthropic**|**claude-3-5-haiku-20241022-v1:0**|`bedrock-2023-05-31`|4e-06 (per 1 token)|1 token\n|**anthropic**|**claude-3-5-sonnet-20240620-v1:0**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-5-sonnet-20241022-v2:0**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-7-sonnet-20240620-v1:0**|`bedrock-2023-05-31`|1.8e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-haiku-20240307-v1:0**|`bedrock-2023-05-31`|1.25e-06 (per 1 token)|1 token\n|**anthropic**|**claude-3-opus-20240229-v1:0**|`bedrock-2023-05-31`|7.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-sonnet-20240229-v1:0**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-instant-v1**|`bedrock-2023-05-31`|2.4e-06 (per 1 token)|1 token\n|**anthropic**|**claude-v1**|`bedrock-2023-05-31`|2.4e-05 (per 1 token)|1 token\n|**anthropic**|**claude-v2:1**|`bedrock-2023-05-31`|2.4e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-5-haiku-20241022**|`bedrock-2023-05-31`|4e-06 (per 1 token)|1 token\n|**anthropic**|**claude-3-5-haiku-latest**|`bedrock-2023-05-31`|5e-06 (per 1 token)|1 token\n|**anthropic**|**claude-haiku-4-5-20251001**|`bedrock-2023-05-31`|5e-06 (per 1 token)|1 token\n|**anthropic**|**claude-haiku-4-5**|`bedrock-2023-05-31`|5e-06 (per 1 token)|1 token\n|**anthropic**|**claude-3-5-sonnet-20240620**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-5-sonnet-20241022**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-5-sonnet-latest**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-7-sonnet-20250219**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-7-sonnet-latest**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-haiku-20240307**|`bedrock-2023-05-31`|1.25e-06 (per 1 token)|1 token\n|**anthropic**|**claude-3-opus-20240229**|`bedrock-2023-05-31`|7.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-3-opus-latest**|`bedrock-2023-05-31`|7.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-4-opus-20250514**|`bedrock-2023-05-31`|7.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-4-sonnet-20250514**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-sonnet-4-5**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-sonnet-4-5-20250929**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-opus-4-1**|`bedrock-2023-05-31`|7.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-opus-4-1-20250805**|`bedrock-2023-05-31`|7.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-opus-4-20250514**|`bedrock-2023-05-31`|7.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-opus-4-5-20251101**|`bedrock-2023-05-31`|2.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-opus-4-5**|`bedrock-2023-05-31`|2.5e-05 (per 1 token)|1 token\n|**anthropic**|**claude-sonnet-4-20250514**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token\n|**xai**|-|`v1`|10.0 (per 1000000 token)|1 token\n|**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**deepseek**|**deepseek-v3**|`v1`|1.1e-06 (per 1 token)|1 token\n|**deepseek**|-|`v1`|1.25 (per 1000000 token)|1 token\n|**deepseek**|**deepseek-reasoner**|`v1`|2.19e-06 (per 1 token)|1 token\n|**deepseek**|**deepseek-chat**|`v1`|1.1e-06 (per 1 token)|1 token\n|**deepseek**|**deepseek-coder**|`v1`|2.8e-07 (per 1 token)|1 token\n|**deepseek**|**deepseek-r1**|`v1`|2.19e-06 (per 1 token)|1 token\n|**deepseek**|**deepseek-v3.2**|`v1`|4e-07 (per 1 token)|1 token\n|**amazon**|-|`boto3 (v1.29.6)`|0.24 (per 1000000 token)|1 token\n|**amazon**|**amazon.nova-lite-v1:0**|`boto3 (v1.29.6)`|0.24 (per 1000000 token)|1 token\n|**amazon**|**amazon.nova-pro-v1:0**|`boto3 (v1.29.6)`|3.2 (per 1000000 token)|1 token\n|**amazon**|**amazon.nova-micro-v1:0**|`boto3 (v1.29.6)`|0.14 (per 1000000 token)|1 token\n|**together_ai**|-|`v1`|1.2 (per 1000000 token)|1 token\n|**together_ai**|**Qwen/Qwen2.5-72B-Instruct-Turbo**|`v1`|0.0 (per 1 seconde)|1 seconde\n|**together_ai**|**meta-llama/Llama-3.3-70B-Instruct-Turbo**|`v1`|8.8e-07 (per 1 token)|1 token\n|**together_ai**|**together-ai-21.1b-41b**|`v1`|8e-07 (per 1 token)|1 token\n|**together_ai**|**together-ai-4.1b-8b**|`v1`|2e-07 (per 1 token)|1 token\n|**together_ai**|**together-ai-41.1b-80b**|`v1`|9e-07 (per 1 token)|1 token\n|**together_ai**|**together-ai-8.1b-21b**|`v1`|3e-07 (per 1 token)|1 token\n|**together_ai**|**together-ai-81.1b-110b**|`v1`|1.8e-06 (per 1 token)|1 token\n|**together_ai**|**together-ai-up-to-4b**|`v1`|1e-07 (per 1 token)|1 token\n|**together_ai**|**Qwen/Qwen2.5-7B-Instruct-Turbo**|`v1`|0.0 (per 1 seconde)|1 seconde\n|**together_ai**|**Qwen/Qwen3-235B-A22B-Instruct-2507-tput**|`v1`|6e-06 (per 1 token)|1 token\n|**together_ai**|**Qwen/Qwen3-235B-A22B-Thinking-2507**|`v1`|3e-06 (per 1 token)|1 token\n|**together_ai**|**Qwen/Qwen3-235B-A22B-fp8-tput**|`v1`|6e-07 (per 1 token)|1 token\n|**together_ai**|**Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8**|`v1`|2e-06 (per 1 token)|1 token\n|**together_ai**|**deepseek-ai/DeepSeek-R1**|`v1`|7e-06 (per 1 token)|1 token\n|**together_ai**|**deepseek-ai/DeepSeek-R1-0528-tput**|`v1`|2.19e-06 (per 1 token)|1 token\n|**together_ai**|**deepseek-ai/DeepSeek-V3**|`v1`|1.25e-06 (per 1 token)|1 token\n|**together_ai**|**deepseek-ai/DeepSeek-V3.1**|`v1`|1.7e-06 (per 1 token)|1 token\n|**together_ai**|**meta-llama/Llama-3.2-3B-Instruct-Turbo**|`v1`|0.0 (per 1 seconde)|1 seconde\n|**together_ai**|**meta-llama/Llama-3.3-70B-Instruct-Turbo-Free**|`v1`|0.0 (per 1 token)|1 token\n|**together_ai**|**meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8**|`v1`|8.5e-07 (per 1 token)|1 token\n|**together_ai**|**meta-llama/Llama-4-Scout-17B-16E-Instruct**|`v1`|5.9e-07 (per 1 token)|1 token\n|**together_ai**|**meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo**|`v1`|3.5e-06 (per 1 token)|1 token\n|**together_ai**|**meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo**|`v1`|8.8e-07 (per 1 token)|1 token\n|**together_ai**|**meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo**|`v1`|1.8e-07 (per 1 token)|1 token\n|**together_ai**|**mistralai/Mistral-7B-Instruct-v0.1**|`v1`|0.0 (per 1 seconde)|1 seconde\n|**together_ai**|**mistralai/Mistral-Small-24B-Instruct-2501**|`v1`|0.0 (per 1 seconde)|1 seconde\n|**together_ai**|**mistralai/Mixtral-8x7B-Instruct-v0.1**|`v1`|6e-07 (per 1 token)|1 token\n|**together_ai**|**moonshotai/Kimi-K2-Instruct**|`v1`|3e-06 (per 1 token)|1 token\n|**together_ai**|**openai/gpt-oss-120b**|`v1`|6e-07 (per 1 token)|1 token\n|**together_ai**|**openai/gpt-oss-20b**|`v1`|2e-07 (per 1 token)|1 token\n|**together_ai**|**togethercomputer/CodeLlama-34b-Instruct**|`v1`|0.0 (per 1 seconde)|1 seconde\n|**together_ai**|**zai-org/GLM-4.5-Air-FP8**|`v1`|1.1e-06 (per 1 token)|1 token\n|**together_ai**|**zai-org/GLM-4.6**|`v1`|2.2e-06 (per 1 token)|1 token\n|**together_ai**|**moonshotai/Kimi-K2-Instruct-0905**|`v1`|3e-06 (per 1 token)|1 token\n|**together_ai**|**Qwen/Qwen3-Next-80B-A3B-Instruct**|`v1`|1.5e-06 (per 1 token)|1 token\n|**together_ai**|**Qwen/Qwen3-Next-80B-A3B-Thinking**|`v1`|1.5e-06 (per 1 token)|1 token\n|**microsoft**|**gpt-4o**|`Azure AI Foundry`|15.0 (per 1000000 token)|1 token\n|**groq**|**llama3-70b-8192**|`v1`|0.59 (per 1000000 token)|1 token\n|**groq**|**llama-3.1-8b-instant**|`v1`|8e-08 (per 1 token)|1 token\n|**groq**|**llama-3.3-70b-versatile**|`v1`|7.9e-07 (per 1 token)|1 token\n|**groq**|**gemma-7b-it**|`v1`|8e-08 (per 1 token)|1 token\n|**groq**|**meta-llama/llama-guard-4-12b**|`v1`|2e-07 (per 1 token)|1 token\n|**groq**|**meta-llama/llama-4-maverick-17b-128e-instruct**|`v1`|6e-07 (per 1 token)|1 token\n|**groq**|**meta-llama/llama-4-scout-17b-16e-instruct**|`v1`|3.4e-07 (per 1 token)|1 token\n|**groq**|**moonshotai/kimi-k2-instruct-0905**|`v1`|3e-06 (per 1 token)|1 token\n|**groq**|**openai/gpt-oss-120b**|`v1`|7.5e-07 (per 1 token)|1 token\n|**groq**|**openai/gpt-oss-20b**|`v1`|5e-07 (per 1 token)|1 token\n|**groq**|**qwen/qwen3-32b**|`v1`|5.9e-07 (per 1 token)|1 token\n\n\n
\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n|**google**|`gemini-2.5-flash`|\n|**cohere**|`command`|\n|**meta**|`llama3-1-8b-instruct-v1:0`|\n|**mistral**|`mistral-large-latest`|\n|**perplexityai**|`sonar`|\n|**anthropic**|`claude-3-5-sonnet-latest`|\n|**xai**|`grok-2-latest`|\n|**deepseek**|`deepseek-chat`|\n|**amazon**|`amazon.nova-lite-v1:0`|\n|**together_ai**|`Qwen/Qwen2.5-72B-Instruct-Turbo`|\n|**microsoft**|`gpt-4o`|\n|**groq**|`llama3-70b-8192`|\n\n
", "summary": "Chat", "tags": [ "Chat" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textchatChatRequest" }, "examples": { "RequestExample": { "value": { "providers": "xai,microsoft,deepseek,perplexityai,anthropic,openai,meta,cohere,amazon,together_ai,google,groq,mistral", "text": "Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.", "chatbot_global_action": "You are a keyword extractor. Extract Only the word from the text provided.", "previous_history": [ { "role": "user", "message": "Steve Jobs was a co-founder of Apple Inc., a multinational technology company headquartered in Cupertino, California. He was also the CEO and a major shareholder of Pixar Animation Studios, which was later acquired by The Walt Disney Company. Jobs was widely recognized as a visionary entrepreneur and a pioneer in the personal computer industry. In addition to his business ventures, he was also known for his charismatic personality, his signature black turtleneck, and his famous keynote presentations at Apple's product launches." }, { "role": "assistant", "message": "steve jobs, apple inc, pixar, california" } ], "temperature": 0.0, "max_tokens": 100 }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textchatResponseModel" }, "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", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "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", "tools": null, "tool_calls": [] } ], "cost": 0.0 }, "microsoft": { "generated_text": "barack obama, american, politician, president, united states, democratic party, african-american, senator, illinois", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "barack obama, american, politician, president, united states, democratic party, african-american, senator, illinois", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 24, "prompt_tokens": 221, "total_tokens": 245, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0 }, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 } }, "cost": 0.0 }, "deepseek": { "generated_text": "barack obama, united states, president, democratic party, illinois, senator", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "barack obama, united states, president, democratic party, illinois, senator", "tools": null, "tool_calls": [] } ], "cost": 0.0 }, "perplexityai": { "generated_text": "barack obama, president, united states, democratic party, african-american, senator, illinois", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "system", "message": "barack obama, president, united states, democratic party, african-american, senator, illinois", "tools": null, "tool_calls": null } ], "usage": null, "cost": 0.0 }, "anthropic": { "generated_text": "barack obama, president, united states, democratic party, illinois, senator, african-american", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "barack obama, president, united states, democratic party, illinois, senator, african-american", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 21, "prompt_tokens": 236, "total_tokens": 257, "completion_tokens_details": null, "prompt_tokens_details": { "audio_tokens": null, "cached_tokens": 0 }, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0 }, "cost": 0.0 }, "openai": { "generated_text": "barack hussein obama, american, politician, 44th president, united states, democratic party, african-american, u.s. senator, illinois, state senator", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "barack hussein obama, american, politician, 44th president, united states, democratic party, african-american, u.s. senator, illinois, state senator", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 37, "prompt_tokens": 222, "total_tokens": 259, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0 }, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 } }, "cost": 0.0 }, "meta": { "generated_text": "\n\nBarack Obama, Democratic Party, United States, Illinois", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "\n\nBarack Obama, Democratic Party, United States, Illinois", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 13, "prompt_tokens": 227, "total_tokens": 240, "completion_tokens_details": null, "prompt_tokens_details": null }, "cost": 0.0 }, "cohere": { "generated_text": "Here are the keywords extracted from the text:\n\n- Steve Jobs\n- Apple Inc.\n- Co-founder\n- Multinational technology company\n- Cupertino\n- California\n- CEO\n- Pixar Animation Studios\n- Walt Disney Company\n- Visionary entrepreneur\n- Pioneer\n- Personal computer industry\n- Charismatic personality\n- Black turtleneck\n- Keynote presentations\n- Barack Hussein Obama\n- American politician\n- 44th President of the United States\n- Democratic Party\n- African-American President\n- U.S. Senator\n- Illinois\n- State Senator.", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "Here are the keywords extracted from the text:\n\n- Steve Jobs\n- Apple Inc.\n- Co-founder\n- Multinational technology company\n- Cupertino\n- California\n- CEO\n- Pixar Animation Studios\n- Walt Disney Company\n- Visionary entrepreneur\n- Pioneer\n- Personal computer industry\n- Charismatic personality\n- Black turtleneck\n- Keynote presentations\n- Barack Hussein Obama\n- American politician\n- 44th President of the United States\n- Democratic Party\n- African-American President\n- U.S. Senator\n- Illinois\n- State Senator.", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 117, "prompt_tokens": 202, "total_tokens": 319, "completion_tokens_details": null, "prompt_tokens_details": null }, "cost": 0.0 }, "amazon": { "generated_text": "barack hussein obama, democratic party, african-american, united states, illinois", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "barack hussein obama, democratic party, african-american, united states, illinois", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 19, "prompt_tokens": 236, "total_tokens": 255, "completion_tokens_details": null, "prompt_tokens_details": null }, "cost": 0.0 }, "together_ai": { "generated_text": "barack obama, united states, president, democratic party, illinois, senator", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "barack obama, united states, president, democratic party, illinois, senator", "tools": null, "tool_calls": [] } ], "cost": 0.0 }, "google": { "generated_text": "Barack Obama, president, United States\n", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "Barack Obama, president, United States\n", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 9, "prompt_tokens": 219, "total_tokens": 228, "completion_tokens_details": null, "prompt_tokens_details": null }, "cost": 0.0 }, "groq": { "generated_text": "barack obama, american, politician, democratic, african, united states, senator, illinois", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "barack obama, american, politician, democratic, african, united states, senator, illinois", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 21, "prompt_tokens": 248, "total_tokens": 269, "completion_tokens_details": null, "prompt_tokens_details": null, "queue_time": 0.094506039, "prompt_time": 0.007630602, "completion_time": 0.028, "total_time": 0.035630602 }, "cost": 0.0 }, "mistral": { "generated_text": "Obama\nAmerican\npresident\nUnited States\nDemocratic\nAfrican\nIllinois\nsenator", "message": [ { "role": "user", "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.", "tools": null, "tool_calls": null }, { "role": "assistant", "message": "Obama\nAmerican\npresident\nUnited States\nDemocratic\nAfrican\nIllinois\nsenator", "tools": null, "tool_calls": [] } ], "usage": { "completion_tokens": 23, "prompt_tokens": 260, "total_tokens": 283, "completion_tokens_details": null, "prompt_tokens_details": null }, "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/chat/stream/": { "post": { "operationId": "text_chat_stream_create", "description": "Streamed version of Chat feature, the raw text will be streamed chunk by chunk.\n\nNOTE: For this feature, you an only request one provider at a time.", "summary": "Chat Stream", "tags": [ "Chat" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textchatChatStreamRequest" }, "examples": { "RequestExample": { "value": { "providers": "xai,microsoft,deepseek,perplexityai,anthropic,openai,meta,cohere,amazon,together_ai,google,groq,mistral", "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.", "chatbot_global_action": "You are a keyword extractor. Extract Only the word from the text provided.", "previous_history": [ { "role": "user", "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." }, { "role": "assistant", "message": "steve jobs, apple inc, pixar, california" } ], "temperature": 0.0, "max_tokens": 100 }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "text/plain": { "schema": { "type": "string" }, "examples": { "ResponseExample": { "value": "{\"text\": \"obama\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"american\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"politician\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"44th\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"president\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"united\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"states\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"democratic\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"party\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"african-american\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"senator\", \"is_blocked\": false, \"provider\": \"openai\"}\n{\"text\": \"illinois\", \"is_blocked\": false, \"provider\": \"openai\"}", "summary": "Response Example" } } } }, "description": "" } } } }, "/text/code_generation/": { "post": { "operationId": "text_code_generation_create", "description": "
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token)|1 token\n|**google**|**gemini-1.5-flash**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-latest**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-latest**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-live-2.5-flash-preview-native-audio-09-2025**|`v1`|2e-06 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-001**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-002**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-8b-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-8b-exp-0924**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-001**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-002**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-exp-0801**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-001**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-exp**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-lite**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-lite-preview-02-05**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-live-001**|`v1`|1.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-preview-image-generation**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-thinking-exp**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-thinking-exp-01-21**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-pro-exp-02-05**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite-preview-09-2025**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-09-2025**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-flash-latest**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-flash-lite-latest**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite-preview-06-17**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-04-17**|`v1`|6e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-05-20**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-tts**|`v1`|6e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-computer-use-preview-10-2025**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-3-pro-preview**|`v1`|1.2e-05 (per 1 token)|1 token\n|**google**|**gemini-3-flash-preview**|`v1`|3e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-exp-03-25**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-03-25**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-05-06**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-06-05**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-tts**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-exp-1114**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-exp-1206**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-gemma-2-27b-it**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-gemma-2-9b-it**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-pro**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-pro-vision**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemma-3-27b-it**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**learnlm-1.5-pro-experimental**|`v1`|0.0 (per 1 token)|1 token\n|**xai**|-|`v1`|10.0 (per 1000000 token)|1 token\n|**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n\n\n
\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n|**google**|`gemini-1.5-flash`|\n|**xai**|`grok-2-latest`|\n\n
", "summary": "Code Generation", "tags": [ "Code Generation" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textcode_generationCodeGenerationRequest" }, "examples": { "RequestExample": { "value": { "providers": "xai,openai,google", "instruction": "Write a function in python that calculates fibonacci", "temperature": 0.1, "max_tokens": 100, "prompt": "" }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textcode_generationResponseModel" }, "examples": { "ResponseExample": { "value": { "xai": { "generated_text": "Sure, here's a Python function that checks if a year is a leap year:\n\n```python\ndef is_leap_year(year):\n \"\"\"\n Returns True if the given year is a leap year, False otherwise.\n \"\"\"\n if year % 4 == 0:\n if year % 100 == 0:\n if year % 400 == 0:\n return True\n else:\n return False\n else:\n return True\n else:\n return False\n```\n\nThis 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.", "cost": 0.0 }, "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.\n\nHere's a Python function to check if a year is a leap year:\n\n```python\ndef is_leap_year(year):\n if year % 4 == 0:\n if year % 100 == 0:\n if year % 400 == 0:\n return True\n else:\n return False\n else:\n return True\n else:\n return False\n```\n\nYou 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:\n\n```python\nprint(is_leap_year(2020)) # Output: True\nprint(is_leap_year(1900)) # Output: False\nprint(is_leap_year(2000)) # Output: True\n```", "usage": { "completion_tokens": 227, "prompt_tokens": 275, "total_tokens": 502, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0 }, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 } }, "cost": 0.0 }, "google": { "generated_text": "```python\ndef is_leap_year(year):\n \"\"\"\n Determines whether a year is a leap year.\n\n Args:\n year: The year to check.\n\n Returns:\n True if the year is a leap year, False otherwise.\n \"\"\"\n\n # A year is a leap year if it is divisible by 4, unless it is divisible by 100\n # unless it is also divisible by 400.\n\n if year % 4 == 0:\n if year % 100 == 0:\n return year % 400 == 0\n else:\n return True\n else:\n return False\n```", "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/embeddings/": { "post": { "operationId": "text_embeddings_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**openai**|-|`v3.0.0`|0.1 (per 1000000 token)|1 token\n|**openai**|**1536__text-embedding-ada-002**|`v3.0.0`|0.1 (per 1000000 token)|1 token\n|**openai**|**text-embedding-3-large**|`v3.0.0`|0.0 (per 1 token)|1 token\n|**openai**|**text-embedding-3-small**|`v3.0.0`|0.0 (per 1 token)|1 token\n|**openai**|**text-embedding-ada-002**|`v3.0.0`|0.0 (per 1 token)|1 token\n|**openai**|**text-embedding-ada-002-v2**|`v3.0.0`|0.0 (per 1 token)|1 token\n|**google**|**gemini-embedding-001**|`v1`|1.5e-07 (per 1 token)|1 token\n|**cohere**|**embed-english-v3.0**|`v1`|0.0 (per 1 token)|1 token\n|**cohere**|**embed-english-light-v3.0**|`v1`|0.0 (per 1 token)|1 token\n|**cohere**|**embed-multilingual-v3.0**|`v1`|0.0 (per 1 token)|1 token\n|**cohere**|**embed-english-v2.0**|`v1`|0.0 (per 1 token)|1 token\n|**cohere**|**embed-english-light-v2.0**|`v1`|0.0 (per 1 token)|1 token\n|**cohere**|**embed-multilingual-v2.0**|`v1`|0.0 (per 1 token)|1 token\n|**cohere**|**embed-v4.0**|`v1`|0.0 (per 1 token)|1 token\n|**cohere**|**embed-multilingual-light-v3.0**|`v1`|0.0 (per 1 token)|1 token\n|**mistral**|**1024__mistral-embed**|`v0.0.1`|0.1 (per 1000000 token)|1 token\n|**mistral**|-|`v0.0.1`|0.1 (per 1000000 token)|1 token\n|**mistral**|**mistral/mistral-embed**|`v0.0.1`|0.1 (per 1000000 seconde)|1 seconde\n|**mistral**|**mistral-embed**|`v0.0.1`|0.0 (per 1 seconde)|1 seconde\n|**mistral**|**codestral-embed**|`v0.0.1`|0.0 (per 1 seconde)|1 seconde\n|**mistral**|**codestral-embed-2505**|`v0.0.1`|0.0 (per 1 seconde)|1 seconde\n|**jina**|-|`v1`|0.018 (per 1000000 token)|1 token\n|**jina**|**jina-embeddings-v2-base-en**|`v1`|0.018 (per 1000000 token)|1 token\n|**jina**|**jina-embeddings-v3**|`v1`|0.02 (per 1000000 token)|1 token\n\n\n
\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`1536__text-embedding-ada-002`|\n|**google**|`text-multilingual-embedding-002`|\n|**cohere**|`4096__embed-english-v2.0`|\n|**mistral**|`1024__mistral-embed`|\n|**jina**|`jina-embeddings-v3`|\n\n
", "summary": "Embeddings", "tags": [ "Embeddings" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textembeddingsEmbeddingsRequest" }, "examples": { "RequestExample": { "value": { "providers": "cohere,mistral,jina,openai,google", "texts": [ "Hello world" ] }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textembeddingsResponseModel" }, "examples": { "ResponseExample": { "value": { "cohere": { "items": [ { "embedding": [ 3.8632812, 0.50927734, 2.2109375, -0.8208008, -0.1071167, -0.22253418, -0.5698242, -0.4506836, 0.43188477, 0.97216797 ] } ], "cost": 0.0 }, "mistral": { "items": [ { "embedding": [ -0.0220947265625, 0.040863037109375, 0.055389404296875, 0.03546142578125, 0.025726318359375, 0.0357666015625, 0.01177978515625, 0.0309600830078125, -0.0310211181640625, -0.0284881591796875 ] } ], "cost": 0.0 }, "jina": { "items": [ { "embedding": [ -0.0047852774, 0.0048640342, -0.01645707, -0.024395779, -0.017263541, 0.012512918, -0.019191515, 0.009053908, -0.010213212, -0.026890801 ] } ], "cost": 0.0 }, "openai": { "items": [ { "embedding": [ -0.0047852774, 0.0048640342, -0.01645707, -0.024395779, -0.017263541, 0.012512918, -0.019191515, 0.009053908, -0.010213212, -0.026890801 ] } ], "cost": 0.0 }, "google": { "items": [ { "embedding": [ 0.027175916358828545, 0.010277487337589264, 0.003436307655647397, 0.02837119624018669, 0.044907715171575546, -0.016265053302049637, 0.010080956853926182, 0.002023588167503476, -0.020589638501405716, 0.012744131498038769 ] }, { "embedding": [ 0.0001314455730607733, 0.015687013044953346, -0.002990683540701866, 0.02059912495315075, 0.028040403500199318, -0.01405489444732666, 0.042016930878162384, 0.013006427325308323, -0.022377343848347664, 0.0183300469070673 ] } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/emotion_detection/": { "post": { "operationId": "text_emotion_detection_create", "description": "
Available Providers\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**vernai**|`v1`|2.0 (per 1000 request)|1 request\n\n\n
\n\n", "summary": "Emotion Detection", "tags": [ "Emotion Detection" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textemotion_detectionEmotionDetectionRequest" }, "examples": { "RequestExample": { "value": { "providers": "vernai", "text": "I'm scared!" }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textemotion_detectionResponseModel" }, "examples": { "ResponseExample": { "value": { "vernai": { "text": "I'm scared", "items": [ { "emotion": "Fear", "emotion_score": 51.0 }, { "emotion": "Sadness", "emotion_score": 51.0 }, { "emotion": "Anger", "emotion_score": 0.0 }, { "emotion": "Love", "emotion_score": 0.0 } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/entity_sentiment/": { "post": { "operationId": "text_entity_sentiment_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**amazon**|-|`boto3 1.26.8`|1.0 (per 1000000 char)|300 char\n|**google**|**gemini-2.0-flash**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-lite**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-03-25**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-latest**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-latest**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-live-2.5-flash-preview-native-audio-09-2025**|`v1`|2e-06 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-001**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-002**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-8b**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-8b-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-8b-exp-0924**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-001**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-002**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-exp-0801**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-001**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-exp**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-lite-preview-02-05**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-live-001**|`v1`|1.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-preview-image-generation**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-thinking-exp**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-thinking-exp-01-21**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-pro-exp-02-05**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite-preview-09-2025**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-09-2025**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-flash-latest**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-flash-lite-latest**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite-preview-06-17**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-04-17**|`v1`|6e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-05-20**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-tts**|`v1`|6e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-computer-use-preview-10-2025**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-3-pro-preview**|`v1`|1.2e-05 (per 1 token)|1 token\n|**google**|**gemini-3-flash-preview**|`v1`|3e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-exp-03-25**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-05-06**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-06-05**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-tts**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-exp-1114**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-exp-1206**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-gemma-2-27b-it**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-gemma-2-9b-it**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-pro**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-pro-vision**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemma-3-27b-it**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**learnlm-1.5-pro-experimental**|`v1`|0.0 (per 1 token)|1 token\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**English**|`en`|\n|**Japanese**|`ja`|\n|**Spanish**|`es`|\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**google**|`gemini-1.5-flash`|\n\n
", "summary": "Entity Sentiment", "tags": [ "Entity Sentiment" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textentity_sentimentEntitySentimentRequest" }, "examples": { "RequestExample": { "value": { "providers": "amazon,google", "language": "en", "text": "Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed. First is the product reviews and pricing. There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product. There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc). The second issue is they make it too difficult to get help when there's an issue with an order." }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textentity_sentimentResponseModel" }, "examples": { "ResponseExample": { "value": { "amazon": { "items": [ { "type": "PERSON", "text": "He", "sentiment": "Neutral", "begin_offset": 223, "end_offset": 225 }, { "type": "PERSON", "text": "Barack Hussein Obama", "sentiment": "Neutral", "begin_offset": 0, "end_offset": 20 }, { "type": "OTHER", "text": "president", "sentiment": "Neutral", "begin_offset": 191, "end_offset": 200 }, { "type": "PERSON", "text": "Obama", "sentiment": "Neutral", "begin_offset": 154, "end_offset": 159 }, { "type": "PERSON", "text": "politician", "sentiment": "Neutral", "begin_offset": 36, "end_offset": 46 }, { "type": "PERSON", "text": "44th", "sentiment": "Neutral", "begin_offset": 65, "end_offset": 69 }, { "type": "OTHER", "text": "president", "sentiment": "Neutral", "begin_offset": 70, "end_offset": 79 }, { "type": "ORGANIZATION", "text": "U.S.", "sentiment": "Neutral", "begin_offset": 249, "end_offset": 253 }, { "type": "ORGANIZATION", "text": "United States", "sentiment": "Neutral", "begin_offset": 87, "end_offset": 100 }, { "type": "ORGANIZATION", "text": "United States", "sentiment": "Neutral", "begin_offset": 208, "end_offset": 221 } ], "cost": 0.0 }, "google": { "items": [ { "type": "PERSON", "text": "Barack Hussein Obama", "sentiment": "Neutral", "begin_offset": null, "end_offset": null }, { "type": "PERSON", "text": "politician", "sentiment": "Neutral", "begin_offset": 36, "end_offset": 46 }, { "type": "PERSON", "text": "Obama", "sentiment": "Neutral", "begin_offset": 154, "end_offset": 159 }, { "type": "PERSON", "text": "member", "sentiment": "Neutral", "begin_offset": 122, "end_offset": 128 }, { "type": "PERSON", "text": "president", "sentiment": "Neutral", "begin_offset": 191, "end_offset": 200 }, { "type": "LOCATION", "text": "American", "sentiment": "Neutral", "begin_offset": 27, "end_offset": 35 }, { "type": "LOCATION", "text": "United States", "sentiment": "Neutral", "begin_offset": 87, "end_offset": 100 }, { "type": "LOCATION", "text": "United States", "sentiment": "Neutral", "begin_offset": 208, "end_offset": 221 }, { "type": "LOCATION", "text": "U.S.", "sentiment": "Neutral", "begin_offset": 249, "end_offset": 253 }, { "type": "PERSON", "text": "president", "sentiment": "Neutral", "begin_offset": 70, "end_offset": 79 } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/keyword_extraction/": { "post": { "operationId": "text_keyword_extraction_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char\n|**microsoft**|-|`v3.1`|1.0 (per 1000000 char)|1000 char\n|**openai**|-|`v3.0.0`|20.0 (per 1000000 token)|1 token\n|**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-mini-2025-04-14**|`v3.0.0`|1.6e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-4.1-nano-2025-04-14**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-4.5-preview**|`v3.0.0`|0.00015 (per 1 token)|1 token\n|**openai**|**gpt-4.5-preview-2025-02-27**|`v3.0.0`|0.00015 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-05-13**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-08-06**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-11-20**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2024-10-01**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2024-12-17**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2025-06-03**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-2024-07-18**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token\n|**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token\n|**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token\n|**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde\n|**tenstorrent**|-|`v1.0.0`|0.7 (per 1000000 char)|1000 char\n|**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Afrikaans**|`af`|\n|**Arabic**|`ar`|\n|**Bulgarian**|`bg`|\n|**Catalan**|`ca`|\n|**Chinese**|`zh`|\n|**Croatian**|`hr`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Estonian**|`et`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**German**|`de`|\n|**Hindi**|`hi`|\n|**Hungarian**|`hu`|\n|**Indonesian**|`id`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Korean**|`ko`|\n|**Latvian**|`lv`|\n|**Modern Greek (1453-)**|`el`|\n|**Norwegian**|`no`|\n|**Norwegian Bokmål**|`nb`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Romanian**|`ro`|\n|**Russian**|`ru`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Spanish**|`es`|\n|**Swedish**|`sv`|\n|**Turkish**|`tr`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (Simplified)**|`zh-Hans`|\n|**Chinese (Taiwan)**|`zh-TW`|\n|**Portuguese (Brazil)**|`pt-BR`|\n|**Portuguese (Portugal)**|`pt-PT`|\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n|**xai**|`grok-2-latest`|\n\n
", "summary": "Keyword Extraction", "tags": [ "Keyword Extraction" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/texttopic_extractiontextanonymizationtextmoderationtextnamed_entity_recognitiontextkeyword_extractiontextsyntax_analysistextsentiment_analysisTextAnalysisRequest" }, "examples": { "RequestExample": { "value": { "providers": "amazon,xai,openai,microsoft,tenstorrent", "language": "en", "text": "Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004." }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textkeyword_extractionResponseModel" }, "examples": { "ResponseExample": { "value": { "amazon": { "items": [ { "keyword": "Barack Hussein Obama", "importance": 1.0 }, { "keyword": "an American politician", "importance": 1.0 }, { "keyword": "the 44th president", "importance": 1.0 }, { "keyword": "the United States", "importance": 1.0 }, { "keyword": "2009 to 2017", "importance": 1.0 }, { "keyword": "A member", "importance": 1.0 }, { "keyword": "the Democratic Party", "importance": 1.0 }, { "keyword": "Obama", "importance": 0.97 }, { "keyword": "the first African-American president", "importance": 1.0 }, { "keyword": "the United States", "importance": 1.0 } ], "cost": 0.0 }, "xai": { "items": [ { "keyword": "Barack Hussein Obama", "importance": 0.9 }, { "keyword": "American politician", "importance": 0.8 }, { "keyword": "44th president", "importance": 0.7 }, { "keyword": "United States", "importance": 0.8 }, { "keyword": "Democratic Party", "importance": 0.6 }, { "keyword": "African-American president", "importance": 0.9 }, { "keyword": "U.S. senator", "importance": 0.7 }, { "keyword": "Illinois state senator", "importance": 0.6 } ], "cost": 0.0 }, "openai": { "items": [ { "keyword": "Barack Hussein Obama", "importance": 1.0 }, { "keyword": "an American politician", "importance": 1.0 }, { "keyword": "the 44th president", "importance": 1.0 }, { "keyword": "the United States", "importance": 1.0 }, { "keyword": "2009 to 2017", "importance": 1.0 }, { "keyword": "the Democratic Party", "importance": 1.0 }, { "keyword": "the first African-American president", "importance": 1.0 }, { "keyword": "a U.S. senator", "importance": 1.0 }, { "keyword": "Illinois", "importance": 1.0 }, { "keyword": "2005 to 2008", "importance": 1.0 } ], "usage": { "completion_tokens": 348, "prompt_tokens": 646, "total_tokens": 994, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0 }, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 } }, "cost": 0.0 }, "microsoft": { "items": [ { "keyword": "U.S. senator", "importance": null }, { "keyword": "first African-American president", "importance": null }, { "keyword": "Barack Hussein Obama", "importance": null }, { "keyword": "Illinois state senator", "importance": null }, { "keyword": "44th president", "importance": null }, { "keyword": "American politician", "importance": null }, { "keyword": "United States", "importance": null }, { "keyword": "Democratic Party", "importance": null }, { "keyword": "member", "importance": null } ], "cost": 0.0 }, "tenstorrent": { "items": [ { "keyword": "barack hussein obama", "importance": 1.0 }, { "keyword": "democratic party", "importance": 0.98 }, { "keyword": "u. s. senator", "importance": 0.98 }, { "keyword": "illinois state", "importance": 0.96 }, { "keyword": "president of the united states", "importance": 0.91 }, { "keyword": "african - american president", "importance": 0.86 } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/moderation/": { "post": { "operationId": "text_moderation_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**microsoft**|-|`v1.0`|1.0 (per 1000 request)|1 request\n|**openai**|-|`v3.0.0`|free|-\n|**openai**|**text-moderation-stable**|`v3.0.0`|0.0 (per 1 token)|1 token\n|**openai**|**text-moderation-007**|`v3.0.0`|0.0 (per 1 token)|1 token\n|**openai**|**text-moderation-latest**|`v3.0.0`|0.0 (per 1 token)|1 token\n|**google**|-|`v1`|5.0 (per 1000000 char)|100 char\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Afrikaans**|`af`|\n|**Albanian**|`sq`|\n|**Amharic**|`am`|\n|**Arabic**|`ar`|\n|**Armenian**|`hy`|\n|**Assamese**|`as`|\n|**Azerbaijani**|`az`|\n|**Basque**|`eu`|\n|**Belarusian**|`be`|\n|**Bengali**|`bn`|\n|**Bosnian**|`bs`|\n|**Breton**|`br`|\n|**Bulgarian**|`bg`|\n|**Catalan**|`ca`|\n|**Central Kurdish**|`ckb`|\n|**Cherokee**|`chr`|\n|**Chinese**|`zh`|\n|**Croatian**|`hr`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Estonian**|`et`|\n|**Filipino**|`fil`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**Fulah**|`ff`|\n|**Galician**|`gl`|\n|**Georgian**|`ka`|\n|**German**|`de`|\n|**Gujarati**|`gu`|\n|**Hausa**|`ha`|\n|**Hebrew**|`he`|\n|**Hindi**|`hi`|\n|**Hungarian**|`hu`|\n|**Icelandic**|`is`|\n|**Igbo**|`ig`|\n|**Indonesian**|`id`|\n|**Inuktitut**|`iu`|\n|**Irish**|`ga`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Kannada**|`kn`|\n|**Kazakh**|`kk`|\n|**Khmer**|`km`|\n|**Kinyarwanda**|`rw`|\n|**Kirghiz**|`ky`|\n|**Konkani (macrolanguage)**|`kok`|\n|**Korean**|`ko`|\n|**Lao**|`lo`|\n|**Latvian**|`lv`|\n|**Lithuanian**|`lt`|\n|**Luxembourgish**|`lb`|\n|**Macedonian**|`mk`|\n|**Malay (macrolanguage)**|`ms`|\n|**Malayalam**|`ml`|\n|**Maltese**|`mt`|\n|**Maori**|`mi`|\n|**Marathi**|`mr`|\n|**Modern Greek (1453-)**|`el`|\n|**Mongolian**|`mn`|\n|**Nepali (macrolanguage)**|`ne`|\n|**Norwegian Bokmål**|`nb`|\n|**Norwegian Nynorsk**|`nn`|\n|**Oriya (macrolanguage)**|`or`|\n|**Panjabi**|`pa`|\n|**Pedi**|`nso`|\n|**Persian**|`fa`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Pushto**|`ps`|\n|**Quechua**|`qu`|\n|**Romanian**|`ro`|\n|**Russian**|`ru`|\n|**Scottish Gaelic**|`gd`|\n|**Serbian**|`sr`|\n|**Sindhi**|`sd`|\n|**Sinhala**|`si`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Southern Sotho**|`st`|\n|**Spanish**|`es`|\n|**Swahili (macrolanguage)**|`sw`|\n|**Swedish**|`sv`|\n|**Tajik**|`tg`|\n|**Tamil**|`ta`|\n|**Tatar**|`tt`|\n|**Telugu**|`te`|\n|**Thai**|`th`|\n|**Tigrinya**|`ti`|\n|**Tswana**|`tn`|\n|**Turkish**|`tr`|\n|**Turkmen**|`tk`|\n|**Uighur**|`ug`|\n|**Ukrainian**|`uk`|\n|**Urdu**|`ur`|\n|**Uzbek**|`uz`|\n|**Vietnamese**|`vi`|\n|**Welsh**|`cy`|\n|**Wolof**|`wo`|\n|**Xhosa**|`xh`|\n|**Yoruba**|`yo`|\n|**Zulu**|`zu`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n\n

Supported Models

\n\n", "summary": "Moderation", "tags": [ "Moderation" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/texttopic_extractiontextanonymizationtextmoderationtextnamed_entity_recognitiontextkeyword_extractiontextsyntax_analysistextsentiment_analysisTextAnalysisRequest" }, "examples": { "RequestExample": { "value": { "providers": "google,openai,microsoft", "language": "en", "text": "Is this a crap email abcdef@abcd.com, phone: 0617730730, IP: 255.255.255.255, 1 Microsoft Way, Redmond, WA 98052" }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textmoderationResponseModel" }, "examples": { "ResponseExample": { "value": { "google": { "nsfw_likelihood": 2, "items": [ { "label": "Toxic", "likelihood": 2, "category": "Toxic", "subcategory": "Toxic", "likelihood_score": 0.2391817 }, { "label": "Insult", "likelihood": 1, "category": "Toxic", "subcategory": "Insult", "likelihood_score": 0.052930057 }, { "label": "Profanity", "likelihood": 2, "category": "Toxic", "subcategory": "Profanity", "likelihood_score": 0.28705063 }, { "label": "Derogatory", "likelihood": 1, "category": "Toxic", "subcategory": "Derogatory", "likelihood_score": 0.0047925273 }, { "label": "Sexual", "likelihood": 1, "category": "Sexual", "subcategory": "Sexual", "likelihood_score": 0.0024352302 }, { "label": "Death, Harm & Tragedy", "likelihood": 1, "category": "Violence", "subcategory": "GraphicViolenceOrGore", "likelihood_score": 0.0005420054 }, { "label": "Violent", "likelihood": 1, "category": "Violence", "subcategory": "Violence", "likelihood_score": 0.0025138261 }, { "label": "Firearms & Weapons", "likelihood": 1, "category": "Violence", "subcategory": "WeaponViolence", "likelihood_score": 0.0 }, { "label": "Public Safety", "likelihood": 1, "category": "Content", "subcategory": "PublicSafety", "likelihood_score": 0.010695187 }, { "label": "Health", "likelihood": 1, "category": "Content", "subcategory": "Health", "likelihood_score": 0.004199916 } ], "nsfw_likelihood_score": 0.28705063, "cost": 0.0 }, "openai": { "nsfw_likelihood": 1, "items": [ { "label": "sexual", "likelihood": 1, "category": "Sexual", "subcategory": "Sexual", "likelihood_score": 8.123514999169856e-05 }, { "label": "hate", "likelihood": 1, "category": "HateAndExtremism", "subcategory": "Hate", "likelihood_score": 0.001375643303617835 }, { "label": "harassment", "likelihood": 1, "category": "HateAndExtremism", "subcategory": "Harassment", "likelihood_score": 0.01840096339583397 }, { "label": "self-harm", "likelihood": 1, "category": "Violence", "subcategory": "PhysicalViolence", "likelihood_score": 0.001366300624795258 }, { "label": "sexual/minors", "likelihood": 1, "category": "Other", "subcategory": "Other", "likelihood_score": 0.00031155155738815665 }, { "label": "hate/threatening", "likelihood": 1, "category": "HateAndExtremism", "subcategory": "Threatening", "likelihood_score": 7.793232362018898e-05 }, { "label": "violence/graphic", "likelihood": 1, "category": "Violence", "subcategory": "GraphicViolenceOrGore", "likelihood_score": 0.0005688181845471263 }, { "label": "self-harm/intent", "likelihood": 1, "category": "Violence", "subcategory": "PhysicalViolence", "likelihood_score": 0.00025184746482409537 }, { "label": "self-harm/instructions", "likelihood": 1, "category": "Violence", "subcategory": "PhysicalViolence", "likelihood_score": 0.0005795329925604165 }, { "label": "harassment/threatening", "likelihood": 1, "category": "HateAndExtremism", "subcategory": "Threatening", "likelihood_score": 9.946248610503972e-05 } ], "nsfw_likelihood_score": 0.01840096339583397, "cost": 0.0 }, "microsoft": { "nsfw_likelihood": 5, "items": [ { "label": "sexually explicit", "likelihood": 1, "category": "Sexual", "subcategory": "Sexual", "likelihood_score": 0.00020882912212982774 }, { "label": "sexually suggestive", "likelihood": 2, "category": "Sexual", "subcategory": "Suggestive", "likelihood_score": 0.2234508991241455 }, { "label": "offensive", "likelihood": 5, "category": "Other", "subcategory": "Offensive", "likelihood_score": 0.9879999756813049 } ], "nsfw_likelihood_score": 0.9879999756813049, "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/named_entity_recognition/": { "post": { "operationId": "text_named_entity_recognition_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char\n|**microsoft**|-|`v3.1`|1.0 (per 1000000 char)|1000 char\n|**openai**|-|`v3.0.0`|10.0 (per 1000000 token)|1 token\n|**openai**|**gpt-4o**|`v3.0.0`|10.0 (per 1000000 token)|1 token\n|**tenstorrent**|-|`v1.0.0`|1.0 (per 1000000 char)|1000 char\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Arabic**|`ar`|\n|**Chinese**|`zh`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**German**|`de`|\n|**Hindi**|`hi`|\n|**Hungarian**|`hu`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Korean**|`ko`|\n|**Norwegian**|`no`|\n|**Norwegian Bokmål**|`nb`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Russian**|`ru`|\n|**Spanish**|`es`|\n|**Swedish**|`sv`|\n|**Turkish**|`tr`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (Simplified)**|`zh-Hans`|\n|**Chinese (Taiwan)**|`zh-TW`|\n|**Chinese (Traditional)**|`zh-Hant`|\n|**Portuguese (Brazil)**|`pt-BR`|\n|**Portuguese (Portugal)**|`pt-PT`|\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n\n
", "summary": "Named Entity Recognition", "tags": [ "Named Entity Recognition" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/texttopic_extractiontextanonymizationtextmoderationtextnamed_entity_recognitiontextkeyword_extractiontextsyntax_analysistextsentiment_analysisTextAnalysisRequest" }, "examples": { "RequestExample": { "value": { "providers": "microsoft,amazon,tenstorrent,openai", "language": "en", "text": "Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004." }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textnamed_entity_recognitionResponseModel" }, "examples": { "ResponseExample": { "value": { "microsoft": { "items": [ { "entity": "Barack Hussein Obama", "category": "PERSON", "importance": 1.0 }, { "entity": "American", "category": "PERSONTYPE", "importance": 0.87 }, { "entity": "politician", "category": "PERSONTYPE", "importance": 0.57 }, { "entity": "44th", "category": "QUANTITY", "importance": 0.8 }, { "entity": "president", "category": "PERSONTYPE", "importance": 0.94 }, { "entity": "United States", "category": "LOCATION", "importance": 0.97 }, { "entity": "from 2009 to 2017", "category": "DATE", "importance": 0.8 }, { "entity": "member", "category": "PERSONTYPE", "importance": 0.74 }, { "entity": "Democratic Party", "category": "ORGANIZATION", "importance": 0.98 }, { "entity": "Obama", "category": "PERSON", "importance": 1.0 } ], "cost": 0.0 }, "amazon": { "items": [ { "entity": "Barack Hussein Obama", "category": "PERSON", "importance": 0.9960151314735413 }, { "entity": "American", "category": "OTHER", "importance": 0.9943374991416931 }, { "entity": "44th president", "category": "QUANTITY", "importance": 0.8183709383010864 }, { "entity": "United States", "category": "LOCATION", "importance": 0.8509750366210938 }, { "entity": "2009", "category": "DATE", "importance": 0.9990425705909729 }, { "entity": "2017", "category": "DATE", "importance": 0.994526207447052 }, { "entity": "Democratic Party", "category": "ORGANIZATION", "importance": 0.9991411566734314 }, { "entity": "Obama", "category": "PERSON", "importance": 0.99968421459198 }, { "entity": "first", "category": "QUANTITY", "importance": 0.9900199174880981 }, { "entity": "African-American", "category": "OTHER", "importance": 0.9452006220817566 } ], "cost": 0.0 }, "tenstorrent": { "items": [ { "entity": "American", "category": "OTHER", "importance": 0.53 }, { "entity": "Barack Hussein Obama", "category": "PERSON", "importance": 0.51 }, { "entity": "African American", "category": "OTHER", "importance": 0.48 }, { "entity": "Democratic Party", "category": "ORGANIZATION", "importance": 0.32 }, { "entity": "United States", "category": "LOCATION", "importance": 0.16 }, { "entity": "Illinois", "category": "LOCATION", "importance": 0.15 }, { "entity": "United States", "category": "LOCATION", "importance": 0.16 }, { "entity": "U. S.", "category": "LOCATION", "importance": 0.08 }, { "entity": "Obama", "category": "PERSON", "importance": 0.04 } ], "cost": 0.0 }, "openai": { "items": [ { "entity": "Barack Hussein Obama", "category": "Person", "importance": 0.98 }, { "entity": "American", "category": "Nationality", "importance": 0.85 }, { "entity": "44th president", "category": "Position", "importance": 0.95 }, { "entity": "United States", "category": "Country", "importance": 0.99 }, { "entity": "Democratic Party", "category": "Organization", "importance": 0.9 }, { "entity": "African-American", "category": "Ethnicity", "importance": 0.85 }, { "entity": "U.S. senator", "category": "Position", "importance": 0.9 }, { "entity": "Illinois", "category": "State", "importance": 0.93 } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/plagia_detection/": { "post": { "operationId": "text_plagia_detection_create", "description": "
Available Providers\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**winstonai**|`v2`|14.0 (per 1000000 char)|1 char\n\n\n
\n\n", "summary": "Plagia Detection", "tags": [ "Plagia Detection" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textplagia_detectionPlagiaDetectionRequest" }, "examples": { "RequestExample": { "value": { "providers": "winstonai", "text": "The Galaxy S23 launch may be far behind us, but Samsung likely has plenty more to announce in 2023. That's if history repeats itself. Should Samsung stick to its annual routine, we can expect to see new foldable phones and wearable devices in August. The company also previewed new designs for bendable phones and tablets earlier this year, hinting that the company may be planning to expand beyond the Z Fold and Z Flip in the near future. Though Samsung regularly releases new products across many categories, including TVs, home appliances and monitors, I'm most interested in where its mobile devices are headed. Samsung is one of the world's largest smartphone manufacturers by market share, meaning it has more influence than most other tech companies on the devices we carry in our pockets each day. Wearables have also become a large part of how Samsung intends to differentiate its phones from those of other Android device makers. It's a strategy to create a web of products that keep people hooked, much like Apple's range of devices.", "title": "n'importe nawak" }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textplagia_detectionResponseModel" }, "examples": { "ResponseExample": { "value": { "winstonai": { "plagia_score": 100.0, "items": [ { "text": "Rumored Samsung Gadgets For 2023: Galaxy Z Flip 5, Z Fold 5 and More - MSN", "candidates": [ { "url": "https://www.msn.com/en-us/news/technology/rumored-samsung-gadgets-for-2023-galaxy-z-flip-5-z-fold-5-and-more/ar-AA16w9Ee", "plagia_score": 1.0, "prediction": "plagiarized", "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." } ] }, { "text": "Originality.ai API Documentation", "candidates": [ { "url": "https://docs.originality.ai/api-v1-0-reference", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." }, { "url": "https://docs.originality.ai/api-v1-0-reference", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." }, { "url": "https://docs.originality.ai/api-v1-0-reference", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." }, { "url": "https://docs.originality.ai/api-v1-0-reference", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." }, { "url": "https://docs.originality.ai/api-v1-0-reference", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." } ] }, { "text": "Rumored Samsung Gadgets For 2023: Galaxy Z Flip 5, Z Fold 5 and More - CNET", "candidates": [ { "url": "https://www.cnet.com/tech/mobile/galaxy-s23-z-fold-5-and-more-samsung-gadgets-rumored-for-2023/", "plagia_score": 1.0, "prediction": "plagiarized", "plagiarized_text": "... Though Samsung regularly releases new products across many categories, including TVs, home appliances and monitors, I'm most interested in ..." }, { "url": "https://www.cnet.com/tech/mobile/galaxy-s23-z-fold-5-and-more-samsung-gadgets-rumored-for-2023/", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." }, { "url": "https://www.cnet.com/tech/mobile/galaxy-s23-z-fold-5-and-more-samsung-gadgets-rumored-for-2023/", "plagia_score": 1.0, "prediction": "plagiarized", "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." } ] }, { "text": "The Biggest Rumored Samsung Gadgets to Expect in 2023 - YouTube", "candidates": [ { "url": "https://www.youtube.com/watch?v=7DXADBWKlgA", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." }, { "url": "https://www.youtube.com/watch?v=7DXADBWKlgA", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." }, { "url": "https://www.youtube.com/watch?v=7DXADBWKlgA", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." } ] }, { "text": "Galaxy Z Fold 5 and Other Samsung Gadgets to Look for in 2023 IT ...", "candidates": [ { "url": "https://www.rbsitsoftwaresolution.com/blog/galaxy-z-fold-5-and-other-samsung-gadgets-to-look-for-in-2023.html", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." } ] }, { "text": "Galaxy S23, Z Fold 5 and more Samsung gadgets rumored for 2023 - US Today News", "candidates": [ { "url": "https://ustoday.news/galaxy-s23-z-fold-5-and-more-samsung-gadgets-rumored-for-2023/", "plagia_score": 1.0, "prediction": "plagiarized", "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 ..." } ] }, { "text": "Galaxy Z Fold 5 and Other Samsung Gadgets to Look for in 2023", "candidates": [ { "url": "https://www.umaconferences.com/galaxy-z-fold-5-and-other-samsung-gadgets-to-look-for-in-2023/", "plagia_score": 1.0, "prediction": "plagiarized", "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." } ] } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/prompt_optimization/": { "post": { "operationId": "text_prompt_optimization_create", "description": "
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token\n|**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 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token\n|**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde\n\n\n
\n\n

Supported Models

\n\n", "summary": "Prompt Optimization", "tags": [ "Prompt Optimization" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textprompt_optimizationPromptOptimizationRequest" }, "examples": { "RequestExample": { "value": { "providers": "openai", "text": "Entity extractor, i give you an entity or multiple entities and a text and i want the entitites extracted from the text", "target_provider": "google" }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textprompt_optimizationResponseModel" }, "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?", "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]." }, { "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." }, { "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." } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/sentiment_analysis/": { "post": { "operationId": "text_sentiment_analysis_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char\n|**google**|**gemini-live-2.5-flash-preview-native-audio-09-2025**|`v1`|2e-06 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-001**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-002**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-latest**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-latest**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-8b**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-8b-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-8b-exp-0924**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-flash-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-001**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-002**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-exp-0801**|`v1`|1.05e-05 (per 1 token)|1 token\n|**google**|**gemini-1.5-pro-exp-0827**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-001**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-exp**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-lite**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-lite-preview-02-05**|`v1`|3e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-live-001**|`v1`|1.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-preview-image-generation**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-thinking-exp**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-flash-thinking-exp-01-21**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.0-pro-exp-02-05**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite-preview-09-2025**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-09-2025**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-flash-latest**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-flash-lite-latest**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-lite-preview-06-17**|`v1`|4e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-04-17**|`v1`|6e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-05-20**|`v1`|2.5e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-flash-preview-tts**|`v1`|6e-07 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-computer-use-preview-10-2025**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-3-pro-preview**|`v1`|1.2e-05 (per 1 token)|1 token\n|**google**|**gemini-3-flash-preview**|`v1`|3e-06 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-exp-03-25**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-03-25**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-05-06**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-06-05**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-2.5-pro-preview-tts**|`v1`|1e-05 (per 1 token)|1 token\n|**google**|**gemini-exp-1114**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-exp-1206**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**gemini-gemma-2-27b-it**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-gemma-2-9b-it**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-pro**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemini-pro-vision**|`v1`|1.05e-06 (per 1 token)|1 token\n|**google**|**gemma-3-27b-it**|`v1`|0.0 (per 1 token)|1 token\n|**google**|**learnlm-1.5-pro-experimental**|`v1`|0.0 (per 1 token)|1 token\n|**microsoft**|-|`v3.1`|1.0 (per 1000000 char)|1000 char\n|**openai**|-|`v3.0.0`|20.0 (per 1000000 token)|1 token\n|**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-mini-2025-04-14**|`v3.0.0`|1.6e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-4.1-nano-2025-04-14**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-4.5-preview**|`v3.0.0`|0.00015 (per 1 token)|1 token\n|**openai**|**gpt-4.5-preview-2025-02-27**|`v3.0.0`|0.00015 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-05-13**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-08-06**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-11-20**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2024-10-01**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2024-12-17**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2025-06-03**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-2024-07-18**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token\n|**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token\n|**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token\n|**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde\n|**tenstorrent**|-|`v1.1.0`|0.7 (per 1000000 char)|1000 char\n|**sapling**|-|`v1`|20.0 (per 1000000 char)|1000 char\n|**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Arabic**|`ar`|\n|**Chinese**|`zh`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**German**|`de`|\n|**Hindi**|`hi`|\n|**Indonesian**|`id`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Korean**|`ko`|\n|**Modern Greek (1453-)**|`el`|\n|**Norwegian**|`no`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Russian**|`ru`|\n|**Spanish**|`es`|\n|**Swedish**|`sv`|\n|**Thai**|`th`|\n|**Turkish**|`tr`|\n|**Vietnamese**|`vi`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (Simplified)**|`zh-Hans`|\n|**Chinese (Taiwan)**|`zh-TW`|\n|**Chinese (Traditional)**|`zh-Hant`|\n|**Portuguese (Brazil)**|`pt-BR`|\n|**Portuguese (Portugal)**|`pt-PT`|\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**google**|`gemini-2.5-flash`|\n|**openai**|`gpt-4o`|\n|**xai**|`grok-2-latest`|\n\n
", "summary": "Sentiment Analysis", "tags": [ "Sentiment Analysis" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/texttopic_extractiontextanonymizationtextmoderationtextnamed_entity_recognitiontextkeyword_extractiontextsyntax_analysistextsentiment_analysisTextAnalysisRequest" }, "examples": { "RequestExample": { "value": { "providers": "sapling,xai,microsoft,tenstorrent,openai,amazon,google", "language": "en", "text": "Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed. First is the product reviews and pricing. There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product. There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc). The second issue is they make it too difficult to get help when there's an issue with an order." }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textsentiment_analysisResponseModel" }, "examples": { "ResponseExample": { "value": { "sapling": { "general_sentiment": "Negative", "general_sentiment_rate": 0.51, "items": [ { "segment": "Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed.", "sentiment": "Positive", "sentiment_rate": 0.96 }, { "segment": "First is the product reviews and pricing.", "sentiment": "Neutral", "sentiment_rate": 1.0 }, { "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.", "sentiment": "Positive", "sentiment_rate": 0.67 }, { "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).", "sentiment": "Neutral", "sentiment_rate": 0.61 }, { "segment": "The second issue is they make it too difficult to get help when there's an issue with an order.", "sentiment": "Negative", "sentiment_rate": 0.96 } ], "cost": 0.0 }, "xai": { "general_sentiment": "Negative", "general_sentiment_rate": 0.3, "items": [], "cost": 0.0 }, "microsoft": { "general_sentiment": "Negative", "general_sentiment_rate": 0.67, "items": [ { "segment": "Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed. ", "sentiment": "Positive", "sentiment_rate": 0.98 }, { "segment": "First is the product reviews and pricing. ", "sentiment": "Neutral", "sentiment_rate": 0.99 }, { "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. ", "sentiment": "Negative", "sentiment_rate": 1.0 }, { "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). ", "sentiment": "Neutral", "sentiment_rate": 0.7 }, { "segment": "The second issue is they make it too difficult to get help when there's an issue with an order.", "sentiment": "Negative", "sentiment_rate": 1.0 } ], "cost": 0.0 }, "tenstorrent": { "general_sentiment": "Negative", "general_sentiment_rate": 0.38, "items": [], "cost": 0.0 }, "openai": { "general_sentiment": "Negative", "general_sentiment_rate": 0.3, "items": [], "cost": 0.0 }, "amazon": { "general_sentiment": "Negative", "general_sentiment_rate": 0.4, "items": [], "cost": 0.0 }, "google": { "general_sentiment": "Negative", "general_sentiment_rate": 0.4, "items": [ { "segment": "Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed.", "sentiment": "Neutral", "sentiment_rate": 0.0 }, { "segment": "First is the product reviews and pricing.", "sentiment": "Neutral", "sentiment_rate": 0.0 }, { "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.", "sentiment": "Negative", "sentiment_rate": 0.5 }, { "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).", "sentiment": "Negative", "sentiment_rate": 0.6 }, { "segment": "The second issue is they make it too difficult to get help when there's an issue with an order.", "sentiment": "Negative", "sentiment_rate": 0.8 } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/spell_check/": { "post": { "operationId": "text_spell_check_create", "description": "
Available Providers\n\n\n\n|Provider|Version|Price|Billing unit|\n|----|-------|-----|------------|\n|**prowritingaid**|`v2`|10.0 (per 1000 request)|1 request\n|**sapling**|`v1`|2.0 (per 1000000 char)|1 char\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Afrikaans**|`af`|\n|**Albanian**|`sq`|\n|**Amharic**|`am`|\n|**Arabic**|`ar`|\n|**Armenian**|`hy`|\n|**Azerbaijani**|`az`|\n|**Basque**|`eu`|\n|**Belarusian**|`be`|\n|**Bengali**|`bn`|\n|**Bosnian**|`bs`|\n|**Bulgarian**|`bg`|\n|**Burmese**|`my`|\n|**Catalan**|`ca`|\n|**Cebuano**|`ceb`|\n|**Chinese**|`zh`|\n|**Corsican**|`co`|\n|**Croatian**|`hr`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Esperanto**|`eo`|\n|**Estonian**|`et`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**Galician**|`gl`|\n|**Georgian**|`ka`|\n|**German**|`de`|\n|**Gujarati**|`gu`|\n|**Haitian**|`ht`|\n|**Hausa**|`ha`|\n|**Hawaiian**|`haw`|\n|**Hebrew**|`he`|\n|**Hindi**|`hi`|\n|**Hmong**|`hmn`|\n|**Hungarian**|`hu`|\n|**Icelandic**|`is`|\n|**Igbo**|`ig`|\n|**Indonesian**|`id`|\n|**Irish**|`ga`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Javanese**|`jv`|\n|**Kannada**|`kn`|\n|**Kazakh**|`kk`|\n|**Khmer**|`km`|\n|**Kinyarwanda**|`rw`|\n|**Kirghiz**|`ky`|\n|**Korean**|`ko`|\n|**Kurdish**|`ku`|\n|**Lao**|`lo`|\n|**Latin**|`la`|\n|**Latvian**|`lv`|\n|**Lithuanian**|`lt`|\n|**Luxembourgish**|`lb`|\n|**Macedonian**|`mk`|\n|**Malagasy**|`mg`|\n|**Malay (macrolanguage)**|`ms`|\n|**Malayalam**|`ml`|\n|**Maltese**|`mt`|\n|**Maori**|`mi`|\n|**Marathi**|`mr`|\n|**Modern Greek (1453-)**|`el`|\n|**Mongolian**|`mn`|\n|**Nepali (macrolanguage)**|`ne`|\n|**Norwegian**|`no`|\n|**Norwegian Bokmål**|`nb`|\n|**Nyanja**|`ny`|\n|**Oriya (macrolanguage)**|`or`|\n|**Panjabi**|`pa`|\n|**Persian**|`fa`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Pushto**|`ps`|\n|**Romanian**|`ro`|\n|**Russian**|`ru`|\n|**Samoan**|`sm`|\n|**Scottish Gaelic**|`gd`|\n|**Serbian**|`sr`|\n|**Shona**|`sn`|\n|**Sindhi**|`sd`|\n|**Sinhala**|`si`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Somali**|`so`|\n|**Southern Sotho**|`st`|\n|**Spanish**|`es`|\n|**Sundanese**|`su`|\n|**Swahili (macrolanguage)**|`sw`|\n|**Swedish**|`sv`|\n|**Tagalog**|`tl`|\n|**Tajik**|`tg`|\n|**Tamil**|`ta`|\n|**Tatar**|`tt`|\n|**Telugu**|`te`|\n|**Thai**|`th`|\n|**Turkish**|`tr`|\n|**Turkmen**|`tk`|\n|**Uighur**|`ug`|\n|**Ukrainian**|`uk`|\n|**Urdu**|`ur`|\n|**Uzbek**|`uz`|\n|**Vietnamese**|`vi`|\n|**Welsh**|`cy`|\n|**Western Frisian**|`fy`|\n|**Xhosa**|`xh`|\n|**Yiddish**|`yi`|\n|**Yoruba**|`yo`|\n|**Zulu**|`zu`|\n|**jp**|`jp`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (China)**|`zh-CN`|\n|**Chinese (Simplified)**|`zh-hans`|\n|**Chinese (Taiwan)**|`zh-TW`|\n|**Chinese (Traditional)**|`zh-hant`|\n|**English (United Kingdom)**|`en-gb`|\n|**Portuguese (Brazil)**|`pt-br`|\n|**Portuguese (Portugal)**|`pt-pt`|\n\n
", "summary": "Spell Check", "tags": [ "Spell Check" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textspell_checkSpellCheckRequest" }, "examples": { "RequestExample": { "value": { "providers": "sapling,prowritingaid", "language": "en", "text": "Hollo, wrld! How re yu?" }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textspell_checkResponseModel" }, "examples": { "ResponseExample": { "value": { "sapling": { "text": "Hollo, wrld! How r yu?", "items": [ { "text": "Hollo", "type": null, "offset": 0, "length": 5, "suggestions": [ { "suggestion": "Hello", "score": null } ] }, { "text": "wrld", "type": null, "offset": 7, "length": 4, "suggestions": [ { "suggestion": "world", "score": null } ] } ], "cost": 0.0 }, "prowritingaid": { "text": "Hollo, wrld! How r yu?", "items": [ { "text": "wrld", "type": "Unknown word: wrld", "offset": 7, "length": 4, "suggestions": [ { "suggestion": "WRLD", "score": null }, { "suggestion": "wild", "score": null }, { "suggestion": "weld", "score": null } ] }, { "text": "yu", "type": "Unknown word: yu", "offset": 19, "length": 2, "suggestions": [ { "suggestion": "you", "score": null }, { "suggestion": "ye", "score": null }, { "suggestion": "ya", "score": null }, { "suggestion": "mu", "score": null }, { "suggestion": "nu", "score": null } ] } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/summarize/": { "post": { "operationId": "text_summarize_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**microsoft**|-|`v3.1`|2.0 (per 1000000 char)|1000 char\n|**openai**|-|`v3.0.0`|60.0 (per 1000000 token)|1 token\n|**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token\n|**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token\n|**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-mini-2025-04-14**|`v3.0.0`|1.6e-06 (per 1 token)|1 token\n|**openai**|**gpt-4.1-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-4.1-nano-2025-04-14**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-4.5-preview**|`v3.0.0`|0.00015 (per 1 token)|1 token\n|**openai**|**gpt-4.5-preview-2025-02-27**|`v3.0.0`|0.00015 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-05-13**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-08-06**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-2024-11-20**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2024-10-01**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2024-12-17**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-audio-preview-2025-06-03**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-2024-07-18**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token\n|**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token\n|**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token\n|**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token\n|**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token\n|**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token\n|**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token\n|**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token\n|**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token\n|**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde\n|**cohere**|-|`2022-12-06`|2.0 (per 1000000 token)|1 token\n|**cohere**|**summarize-xlarge**|`2022-12-06`|2.0 (per 1000000 token)|1 token\n|**cohere**|**command-nightly**|`2022-12-06`|2.0 (per 1000000 token)|1 token\n|**cohere**|**command-nightly**|`2022-12-06`|2.0 (per 1000000 token)|1 token\n|**xai**|**grok-2-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-1212**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-1212**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-2-vision-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token\n|**xai**|**grok-3**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-beta**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-beta**|`v3.0.0`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-fast-latest**|`v3.0.0`|2.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-3-mini**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-beta**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-beta**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-fast-latest**|`v3.0.0`|4e-06 (per 1 token)|1 token\n|**xai**|**grok-3-mini-latest**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-fast-reasoning**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-fast-non-reasoning**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-0709**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-reasoning-latest**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-4-1-fast-non-reasoning-latest**|`v3.0.0`|5e-07 (per 1 token)|1 token\n|**xai**|**grok-beta**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n|**xai**|**grok-code-fast**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-code-fast-1-0825**|`v3.0.0`|1.5e-06 (per 1 token)|1 token\n|**xai**|**grok-vision-beta**|`v3.0.0`|1.5e-05 (per 1 token)|1 token\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Bulgarian**|`bg`|\n|**Chinese**|`zh`|\n|**Czech**|`cs`|\n|**Danish**|`da`|\n|**Dutch**|`nl`|\n|**English**|`en`|\n|**Estonian**|`et`|\n|**Finnish**|`fi`|\n|**French**|`fr`|\n|**German**|`de`|\n|**Hungarian**|`hu`|\n|**Italian**|`it`|\n|**Japanese**|`ja`|\n|**Korean**|`ko`|\n|**Latvian**|`lv`|\n|**Modern Greek (1453-)**|`el`|\n|**Polish**|`pl`|\n|**Portuguese**|`pt`|\n|**Romanian**|`ro`|\n|**Russian**|`ru`|\n|**Slovak**|`sk`|\n|**Slovenian**|`sl`|\n|**Spanish**|`es`|\n|**Swedish**|`sv`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n|**Chinese (Simplified)**|`zh-Hans`|\n|**Portuguese (Brazil)**|`pt-BR`|\n|**Portuguese (Brazil)**|`pt-br`|\n|**Portuguese (Portugal)**|`pt-PT`|\n|**Portuguese (Portugal)**|`pt-pt`|\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4`|\n|**cohere**|`summarize-xlarge`|\n|**xai**|`grok-2-latest`|\n\n
", "summary": "Summarize", "tags": [ "Summarize" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textsummarizeSummarizeRequest" }, "examples": { "RequestExample": { "value": { "providers": "microsoft,openai,xai,cohere", "output_sentences": 3, "text": "Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.", "language": "en" }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/textsummarizeResponseModel" }, "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.", "cost": 0.0 }, "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.", "usage": { "completion_tokens": 82, "prompt_tokens": 300, "total_tokens": 382, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0 }, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 } }, "cost": 0.0 }, "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.", "cost": 0.0 }, "cohere": { "result": "\"You have to have a big-picture vision for where you want to go.", "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } }, "/text/topic_extraction/": { "post": { "operationId": "text_topic_extraction_create", "description": "
Available Providers\n\n\n\n|Provider|Model|Version|Price|Billing unit|\n|----|----|-------|-----|------------|\n|**google**|-|`v1`|0.6 (per 1000000 char)|1 char\n|**openai**|**gpt-4o**|`v1`|10.0 (per 1000000 token)|1 token\n|**openai**|-|`v1`|10.0 (per 1000000 token)|1 token\n|**tenstorrent**|-|`v1.0.0`|2.0 (per 1000000 char)|1000 char\n\n\n
\n\n
Supported Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**English**|`en`|\n\n
Supported Detailed Languages\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**Auto detection**|`auto-detect`|\n\n

Supported Models

\n\n
Default Models\n\n\n\n\n\n|Name|Value|\n|----|-----|\n|**openai**|`gpt-4o`|\n\n
", "summary": "Topic Extraction", "tags": [ "Topic Extraction" ], "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/texttopic_extractiontextanonymizationtextmoderationtextnamed_entity_recognitiontextkeyword_extractiontextsyntax_analysistextsentiment_analysisTextAnalysisRequest" }, "examples": { "RequestExample": { "value": { "providers": "tenstorrent,google,openai", "language": "en", "text": "That actor on TV makes movies in Hollywood and also stars in a variety of popular new TV shows." }, "summary": "Request Example" } } } }, "required": true }, "security": [ { "FeatureApiAuth": [] } ], "responses": { "200": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/texttopic_extractionResponseModel" }, "examples": { "ResponseExample": { "value": { "tenstorrent": { "items": [ { "category": "Film Tv & Video", "importance": 0.99 }, { "category": "Celebrity & Pop Culture", "importance": 0.79 } ], "cost": 0.0 }, "google": { "items": [ { "category": "/Arts & Entertainment/Tv & Video/Tv Shows & Programs", "importance": 0.52 } ], "cost": 0.0 }, "openai": { "items": [ { "category": "Entertainment", "importance": 0.9 }, { "category": "Film Industry", "importance": 0.8 }, { "category": "Television", "importance": 0.7 } ], "cost": 0.0 } }, "summary": "Response Example" } } } }, "description": "" }, "400": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BadRequest" } } }, "description": "" }, "500": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "403": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Error" } } }, "description": "" }, "404": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NotFoundResponse" } } }, "description": "" } } } } }, "components": { "schemas": { "AiDetectionItem": { "properties": { "text": { "title": "Text", "type": "string" }, "prediction": { "title": "Prediction", "type": "string" }, "ai_score": { "title": "Ai Score", "type": "integer" }, "ai_score_detail": { "title": "Ai Score Detail", "type": "integer" } }, "required": [ "text", "prediction", "ai_score", "ai_score_detail" ], "title": "AiDetectionItem", "type": "object" }, "AnonymizationEntity": { "description": "This model represents an entity extracted from the text.\n\nAttributes:\n offset (int): The offset of the entity in the text.\n length (int): The lenght of the entity in the text.\n category (CategoryType): The category of the entity.\n subcategory (SubCategoryType): The subcategory of the entity.\n original_label (str): The original label of the entity.\n content (str): The content of the entity.", "properties": { "offset": { "minimum": 0, "title": "Offset", "type": "integer" }, "length": { "exclusiveMinimum": true, "title": "Length", "type": "integer" }, "category": { "$ref": "#/components/schemas/CategoryType" }, "subcategory": { "$ref": "#/components/schemas/SubCategoryType" }, "original_label": { "minLength": 1, "title": "Original Label", "type": "string" }, "content": { "minLength": 1, "title": "Content", "type": "string" }, "confidence_score": { "maximum": 1, "minimum": 0, "title": "Confidence Score", "type": "integer" } }, "required": [ "offset", "length", "category", "subcategory", "original_label", "content", "confidence_score" ], "title": "AnonymizationEntity", "type": "object" }, "BadRequest": { "type": "object", "properties": { "error": { "$ref": "#/components/schemas/NestedBadRequest" } }, "required": [ "error" ] }, "CategoryType": { "description": "This enum are used to categorize the entities extracted from the text.", "enum": [ "PersonalInformation", "FinancialInformation", "IdentificationNumbers", "Miscellaneous", "OrganizationInformation", "DateAndTime", "LocationInformation", "Other" ], "title": "CategoryType", "type": "string" }, "ChatAvailableToolsRequest": { "type": "object", "properties": { "name": { "type": "string", "minLength": 1, "description": "The name of your tool/function" }, "description": { "type": "string" }, "parameters": { "description": "The tool's parameters are specified using a JSON Schema object. Detailed format documentation is available in the [JSON Schema reference](https://json-schema.org/understanding-json-schema/).\n\n**Make sure to well describe each parameter for best results.**\n\n\nExample for a weather tool:\n\n {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\"\n \"description\": \"The geographical location for which weather data is requested.\"\n },\n \"unit\": {\n \"type\": \"string\", \"enum\": [\"Celsius\", \"Fahrenheit\"]\n \"description\": \"The unit of measurement for temperature.\"\n }\n },\n \"required\": [\"location\"]\n }\n " } } }, "ChatMessageDataClass": { "properties": { "role": { "title": "Role", "type": "string" }, "message": { "default": "", "title": "Message", "type": "string" }, "tools": { "default": null, "description": "Tools defined by the user", "items": { "additionalProperties": true, "type": "object" }, "title": "Tools", "type": "array" }, "tool_calls": { "default": null, "description": "The tools arguments generated from tools definition and user prompt.", "items": { "$ref": "#/components/schemas/ToolCall" }, "title": "Tool Calls", "type": "array" } }, "required": [ "role" ], "title": "ChatMessageDataClass", "type": "object" }, "ChatMessageRequest": { "type": "object", "properties": { "role": { "type": "string", "minLength": 1 }, "message": {}, "tools": { "type": "array", "items": { "$ref": "#/components/schemas/ChatAvailableToolsRequest" }, "nullable": true }, "tool_calls": { "type": "array", "items": { "$ref": "#/components/schemas/ChatToolCallsRequest" }, "nullable": true } }, "required": [ "message", "role" ] }, "ChatToolCallsRequest": { "type": "object", "properties": { "id": { "type": "string", "minLength": 1 }, "name": { "type": "string", "minLength": 1 }, "arguments": { "type": "string", "minLength": 1 } }, "required": [ "arguments", "id", "name" ] }, "ChatToolResultRequest": { "type": "object", "properties": { "id": { "type": "string", "minLength": 1, "description": "the id of the `tool_call` used to generate result" }, "result": { "type": "string", "minLength": 1, "description": "the result of your function" } }, "required": [ "id", "result" ] }, "EmbeddingDataClass": { "properties": { "embedding": { "items": { "type": "integer" }, "title": "Embedding", "type": "array" } }, "required": [ "embedding" ], "title": "EmbeddingDataClass", "type": "object" }, "EmotionItem": { "description": "This class is used in EmotionAnalysisDataClass to list emotion analysed.\nArgs:\n - emotion (EmotionEnum): emotion of the text\n - emotion_score (float): score of the emotion", "properties": { "emotion": { "title": "Emotion", "type": "string" }, "emotion_score": { "maximum": 100, "minimum": 0, "title": "Emotion Score", "type": "integer" } }, "required": [ "emotion", "emotion_score" ], "title": "EmotionItem", "type": "object" }, "Entity": { "properties": { "type": { "description": "Recognized Entity type", "title": "Type", "type": "string" }, "text": { "description": "Text corresponding to the entity", "title": "Text", "type": "string" }, "sentiment": { "allOf": [ { "$ref": "#/components/schemas/EntitySentimentEnum" } ], "title": "Sentiment" }, "begin_offset": { "default": null, "title": "Begin Offset", "type": "integer" }, "end_offset": { "default": null, "title": "End Offset", "type": "integer" } }, "required": [ "type", "text", "sentiment" ], "title": "Entity", "type": "object" }, "EntitySentimentEnum": { "enum": [ "Positive", "Negative", "Neutral", "Mixed" ], "type": "string" }, "Error": { "type": "object", "properties": { "error": { "$ref": "#/components/schemas/NestedError" } }, "required": [ "error" ] }, "ExtractedTopic": { "properties": { "category": { "title": "Category", "type": "string" }, "importance": { "title": "Importance", "type": "integer" } }, "required": [ "category", "importance" ], "title": "ExtractedTopic", "type": "object" }, "FallbackTypeEnum": { "enum": [ "rerun", "continue" ], "type": "string", "description": "* `rerun` - Rerun\n* `continue` - Continue" }, "FieldError": { "type": "object", "properties": { "": { "type": "array", "items": { "type": "string" } } }, "required": [ "" ] }, "GeneralSentimentEnum": { "enum": [ "Positive", "Negative", "Neutral" ], "type": "string" }, "InfosKeywordExtractionDataClass": { "properties": { "keyword": { "title": "Keyword", "type": "string" }, "importance": { "title": "Importance", "type": "integer" } }, "required": [ "keyword", "importance" ], "title": "InfosKeywordExtractionDataClass", "type": "object" }, "InfosNamedEntityRecognitionDataClass": { "properties": { "entity": { "title": "Entity", "type": "string" }, "category": { "title": "Category", "type": "string" }, "importance": { "title": "Importance", "type": "integer" } }, "required": [ "entity", "category", "importance" ], "title": "InfosNamedEntityRecognitionDataClass", "type": "object" }, "NestedBadRequest": { "type": "object", "properties": { "type": { "type": "string" }, "message": { "$ref": "#/components/schemas/FieldError" } }, "required": [ "message", "type" ] }, "NestedError": { "type": "object", "properties": { "type": { "type": "string" }, "message": { "type": "string" } }, "required": [ "message", "type" ] }, "NotFoundResponse": { "type": "object", "properties": { "details": { "type": "string", "default": "Not Found" } } }, "PlagiaDetectionCandidate": { "properties": { "url": { "title": "Url", "type": "string" }, "plagia_score": { "title": "Plagia Score", "type": "integer" }, "prediction": { "title": "Prediction", "type": "string" }, "plagiarized_text": { "title": "Plagiarized Text", "type": "string" } }, "required": [ "url", "plagia_score", "prediction", "plagiarized_text" ], "title": "PlagiaDetectionCandidate", "type": "object" }, "PlagiaDetectionItem": { "properties": { "text": { "title": "Text", "type": "string" }, "candidates": { "items": { "$ref": "#/components/schemas/PlagiaDetectionCandidate" }, "title": "Candidates", "type": "array" } }, "required": [ "text" ], "title": "PlagiaDetectionItem", "type": "object" }, "PromptDataClass": { "properties": { "text": { "title": "Text", "type": "string" } }, "required": [ "text" ], "title": "PromptDataClass", "type": "object" }, "ReasoningEffortEnum": { "enum": [ "low", "medium", "high" ], "type": "string", "description": "**Choices**:\n- 'low': Minimal reasoning, quick responses\n- 'medium': Balanced reasoning approach\n- 'high': In-depth, comprehensive reasoning\n\n**Example**: 'high' for complex problem-solving tasks\n\n* `low` - low\n* `medium` - medium\n* `high` - high" }, "SegmentSentimentAnalysisDataClass": { "description": "This class is used in SentimentAnalysisDataClass to describe each segment analyzed.\n\nArgs:\n - segment (str): The segment analyzed\n - sentiment (Literal['Positve', 'Negative', 'Neutral']) (Case is ignore): Sentiment of segment\n - sentiment_rate (float between 0 and 1): Rate of sentiment", "properties": { "segment": { "title": "Segment", "type": "string" }, "sentiment": { "allOf": [ { "$ref": "#/components/schemas/SentimentEbfEnum" } ], "title": "Sentiment" }, "sentiment_rate": { "maximum": 1, "minimum": 0, "title": "Sentiment Rate", "type": "integer" } }, "required": [ "segment", "sentiment", "sentiment_rate" ], "title": "SegmentSentimentAnalysisDataClass", "type": "object" }, "SentimentEbfEnum": { "enum": [ "Positive", "Negative", "Neutral" ], "type": "string" }, "SpellCheckItem": { "description": "Represents a spell check item with suggestions.\n\nArgs:\n text (str): The text to spell check.\n type (str, optional): The type of the text.\n offset (int): The offset of the text.\n length (int): The length of the text.\n suggestions (Sequence[SuggestionItem], optional): The list of suggestions for the misspelled text.\n\nRaises:\n ValueError: If the offset or length is not positive.\n\nReturns:\n SpellCheckItem: An instance of the SpellCheckItem class.", "properties": { "text": { "title": "Text", "type": "string" }, "type": { "title": "Type", "type": "string" }, "offset": { "minimum": 0, "title": "Offset", "type": "integer" }, "length": { "minimum": 0, "title": "Length", "type": "integer" }, "suggestions": { "items": { "$ref": "#/components/schemas/SuggestionItem" }, "title": "Suggestions", "type": "array" } }, "required": [ "text", "type", "offset", "length" ], "title": "SpellCheckItem", "type": "object" }, "StatusEnum": { "enum": [ "sucess", "fail" ], "type": "string" }, "SubCategoryType": { "enum": [ "CreditCard", "CardExpiry", "BankAccountNumber", "BankRoutingNumber", "SwiftCode", "TaxIdentificationNumber", "Name", "Age", "Email", "Phone", "PersonType", "Gender", "SocialSecurityNumber", "NationalIdentificationNumber", "NationalHealthService", "ResidentRegistrationNumber", "DriverLicenseNumber", "PassportNumber", "URL", "IP", "MAC", "VehicleIdentificationNumber", "LicensePlate", "VoterNumber", "AWSKeys", "AzureKeys", "Password", "CompanyName", "CompanyNumber", "BuisnessNumber", "Date", "Time", "DateTime", "Duration", "Address", "Location", "Other", "Anonymized", "Nerd", "Wsd", "Unknown" ], "title": "SubCategoryType", "type": "string" }, "SuggestionItem": { "description": "Represents a suggestion for a misspelled word.\n\nArgs:\n suggestion (str): The suggested text.\n score (float, optional): The score of the suggested text (between 0 and 1).\n\nRaises:\n ValueError: If the score is not between 0 and 1.\n\nReturns:\n SuggestionItem: An instance of the SuggestionItem class.", "properties": { "suggestion": { "title": "Suggestion", "type": "string" }, "score": { "maximum": 1, "minimum": 0, "title": "Score", "type": "integer" } }, "required": [ "suggestion", "score" ], "title": "SuggestionItem", "type": "object" }, "TextModerationItem": { "properties": { "label": { "title": "Label", "type": "string" }, "likelihood": { "title": "Likelihood", "type": "integer" }, "category": { "$ref": "#/components/schemas/CategoryType" }, "subcategory": { "$ref": "#/components/schemas/SubCategoryType" }, "likelihood_score": { "title": "Likelihood Score", "type": "integer" } }, "required": [ "label", "likelihood", "category", "subcategory", "likelihood_score" ], "title": "TextModerationItem", "type": "object" }, "ToolCall": { "properties": { "id": { "title": "Id", "type": "string" }, "name": { "title": "Name", "type": "string" }, "arguments": { "title": "Arguments", "type": "string" } }, "required": [ "id", "name", "arguments" ], "title": "ToolCall", "type": "object" }, "ToolChoiceEnum": { "enum": [ "auto", "required", "none" ], "type": "string", "description": "* `auto` - auto\n* `required` - required\n* `none` - none" }, "textai_detectionAiDetectionDataClass": { "properties": { "ai_score": { "title": "Ai Score", "type": "integer" }, "items": { "items": { "$ref": "#/components/schemas/AiDetectionItem" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "ai_score", "status" ], "title": "textai_detectionAiDetectionDataClass", "type": "object" }, "textai_detectionAiDetectionRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "provider_params": { "type": "string", "description": "\nParameters specific to the provider that you want to send along the request.\n\nit should take a *provider* name as key and an object of parameters as value.\n\nExample:\n\n {\n \"deepgram\": {\n \"filler_words\": true,\n \"smart_format\": true,\n \"callback\": \"https://webhook.site/0000\"\n },\n \"assembly\": {\n \"webhook_url\": \"https://webhook.site/0000\"\n }\n }\n\nPlease refer to the documentation of each provider to see which parameters to send.\n" }, "text": { "type": "string", "minLength": 1, "description": "Text to analyze" } }, "required": [ "providers", "text" ] }, "textai_detectionResponseModel": { "properties": { "sapling": { "$ref": "#/components/schemas/textai_detectionAiDetectionDataClass", "default": null }, "originalityai": { "$ref": "#/components/schemas/textai_detectionAiDetectionDataClass", "default": null }, "winstonai": { "$ref": "#/components/schemas/textai_detectionAiDetectionDataClass", "default": null } }, "title": "textai_detectionResponseModel", "type": "object" }, "textanonymizationAnonymizationDataClass": { "properties": { "result": { "title": "Result", "type": "string" }, "entities": { "items": { "$ref": "#/components/schemas/AnonymizationEntity" }, "title": "Entities", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "result", "status" ], "title": "textanonymizationAnonymizationDataClass", "type": "object" }, "textanonymizationResponseModel": { "properties": { "xai": { "$ref": "#/components/schemas/textanonymizationAnonymizationDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textanonymizationAnonymizationDataClass", "default": null }, "oneai": { "$ref": "#/components/schemas/textanonymizationAnonymizationDataClass", "default": null }, "privateai": { "$ref": "#/components/schemas/textanonymizationAnonymizationDataClass", "default": null }, "microsoft": { "$ref": "#/components/schemas/textanonymizationAnonymizationDataClass", "default": null }, "amazon": { "$ref": "#/components/schemas/textanonymizationAnonymizationDataClass", "default": null }, "emvista": { "$ref": "#/components/schemas/textanonymizationAnonymizationDataClass", "default": null } }, "title": "textanonymizationResponseModel", "type": "object" }, "textchatChatDataClass": { "properties": { "generated_text": { "title": "Generated Text", "type": "string" }, "message": { "items": { "$ref": "#/components/schemas/ChatMessageDataClass" }, "title": "Message", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "generated_text", "status" ], "title": "textchatChatDataClass", "type": "object" }, "textchatChatRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "nullable": true, "default": "", "description": "Start your conversation here..." }, "chatbot_global_action": { "type": "string", "nullable": true, "default": "", "description": "A system message that helps set the behavior of the assistant. For example, 'You are a helpful assistant'." }, "previous_history": { "type": "array", "items": { "$ref": "#/components/schemas/ChatMessageRequest" }, "description": "A list containing all the previous conversations between the user and the chatbot AI. Each item in the list should be a dictionary with two keys: 'role' and 'message'. The 'role' key specifies the role of the speaker and can have the values 'user' or 'assistant'. The 'message' key contains the text of the conversation from the respective role. For example: [{'role': 'user', 'message': 'Hello'}, {'role': 'assistant', 'message': 'Hi, how can I help you?'}, ...]. This format allows easy identification of the speaker's role and their corresponding message." }, "temperature": { "type": "number", "format": "double", "maximum": 2, "minimum": 0, "default": 0.0, "description": "Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer." }, "max_tokens": { "type": "integer", "minimum": 1, "default": 4096, "description": "The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length." }, "tool_choice": { "allOf": [ { "$ref": "#/components/schemas/ToolChoiceEnum" } ], "default": "auto", "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\n\n* `auto` - auto\n* `required` - required\n* `none` - none" }, "available_tools": { "type": "array", "items": { "$ref": "#/components/schemas/ChatAvailableToolsRequest" }, "description": "A list of tools the model may generate the right arguments for." }, "tool_results": { "type": "array", "items": { "$ref": "#/components/schemas/ChatToolResultRequest" }, "description": "List of results obtained from applying the tool_call arguments to your own tool." }, "reasoning_effort": { "allOf": [ { "$ref": "#/components/schemas/ReasoningEffortEnum" } ], "description": "Optional parameter to control the model's reasoning depth. \nAllows specifying the level of analytical effort in generating responses. \n\n**Choices**:\n- 'low': Minimal reasoning, quick responses\n- 'medium': Balanced reasoning approach\n- 'high': In-depth, comprehensive reasoning\n\n**Example**: 'high' for complex problem-solving tasks\n\n* `low` - low\n* `medium` - medium\n* `high` - high" } }, "required": [ "providers" ] }, "textchatChatStreamRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "nullable": true, "default": "", "description": "Start your conversation here..." }, "chatbot_global_action": { "type": "string", "nullable": true, "default": "", "description": "A system message that helps set the behavior of the assistant. For example, 'You are a helpful assistant'." }, "previous_history": { "type": "array", "items": { "$ref": "#/components/schemas/ChatMessageRequest" }, "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." }, "temperature": { "type": "number", "format": "double", "maximum": 2, "minimum": 0, "default": 0.0, "description": "Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer." }, "max_tokens": { "type": "integer", "minimum": 1, "default": 4096, "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." }, "tool_choice": { "allOf": [ { "$ref": "#/components/schemas/ToolChoiceEnum" } ], "default": "auto", "description": "`auto`: the model will choose to use tools if needed, `required`: force model to use any of the available tools, `none`: force model to not select a tool\n\n* `auto` - auto\n* `required` - required\n* `none` - none" }, "available_tools": { "type": "array", "items": { "$ref": "#/components/schemas/ChatAvailableToolsRequest" }, "description": "A list of tools the model may generate the right arguments for." }, "tool_results": { "type": "array", "items": { "$ref": "#/components/schemas/ChatToolResultRequest" }, "description": "List of results obtained from applying the tool_call arguments to your own tool." }, "reasoning_effort": { "allOf": [ { "$ref": "#/components/schemas/ReasoningEffortEnum" } ], "description": "Optional parameter to control the model's reasoning depth. \nAllows specifying the level of analytical effort in generating responses. \n\n**Choices**:\n- 'low': Minimal reasoning, quick responses\n- 'medium': Balanced reasoning approach\n- 'high': In-depth, comprehensive reasoning\n\n**Example**: 'high' for complex problem-solving tasks\n\n* `low` - low\n* `medium` - medium\n* `high` - high" }, "fallback_type": { "allOf": [ { "$ref": "#/components/schemas/FallbackTypeEnum" } ], "default": "continue" } }, "required": [ "providers" ] }, "textchatResponseModel": { "properties": { "xai": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "deepseek": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "anthropic": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "perplexityai": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "groq": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "together_ai": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "microsoft": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "meta": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "cohere": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "replicate": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "mistral": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "amazon": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null }, "google": { "$ref": "#/components/schemas/textchatChatDataClass", "default": null } }, "title": "textchatResponseModel", "type": "object" }, "textcode_generationCodeGenerationDataClass": { "properties": { "generated_text": { "title": "Generated Text", "type": "string" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "generated_text", "status" ], "title": "textcode_generationCodeGenerationDataClass", "type": "object" }, "textcode_generationCodeGenerationRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "prompt": { "type": "string", "nullable": true, "default": "", "description": "Entrer the source code that will be used as a context." }, "instruction": { "type": "string", "minLength": 1, "description": "Entrer the instruction you want to be followed." }, "temperature": { "type": "number", "format": "double", "maximum": 1, "minimum": 0, "default": 0.0, "description": "Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer." }, "max_tokens": { "type": "integer", "minimum": 1, "default": 1000, "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." } }, "required": [ "instruction", "providers" ] }, "textcode_generationResponseModel": { "properties": { "xai": { "$ref": "#/components/schemas/textcode_generationCodeGenerationDataClass", "default": null }, "google": { "$ref": "#/components/schemas/textcode_generationCodeGenerationDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textcode_generationCodeGenerationDataClass", "default": null } }, "title": "textcode_generationResponseModel", "type": "object" }, "textembeddingsEmbeddingsDataClass": { "properties": { "items": { "items": { "$ref": "#/components/schemas/EmbeddingDataClass" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "status" ], "title": "textembeddingsEmbeddingsDataClass", "type": "object" }, "textembeddingsEmbeddingsRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "texts": { "type": "array", "items": { "type": "string", "minLength": 1 }, "description": "List of texts to transform into embeddings." }, "dimensions": { "type": "integer", "minimum": 1, "nullable": true, "description": " parameter to control the size of the output embedding vecto" } }, "required": [ "providers", "texts" ] }, "textembeddingsResponseModel": { "properties": { "ai21labs": { "$ref": "#/components/schemas/textembeddingsEmbeddingsDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textembeddingsEmbeddingsDataClass", "default": null }, "jina": { "$ref": "#/components/schemas/textembeddingsEmbeddingsDataClass", "default": null }, "cohere": { "$ref": "#/components/schemas/textembeddingsEmbeddingsDataClass", "default": null }, "mistral": { "$ref": "#/components/schemas/textembeddingsEmbeddingsDataClass", "default": null }, "iointelligence": { "$ref": "#/components/schemas/textembeddingsEmbeddingsDataClass", "default": null }, "google": { "$ref": "#/components/schemas/textembeddingsEmbeddingsDataClass", "default": null } }, "title": "textembeddingsResponseModel", "type": "object" }, "textemotion_detectionEmotionDetectionDataClass": { "properties": { "text": { "title": "Text", "type": "string" }, "items": { "items": { "$ref": "#/components/schemas/EmotionItem" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "text", "status" ], "title": "textemotion_detectionEmotionDetectionDataClass", "type": "object" }, "textemotion_detectionEmotionDetectionRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "minLength": 1, "description": "Text to analyze" } }, "required": [ "providers", "text" ] }, "textemotion_detectionResponseModel": { "properties": { "vernai": { "$ref": "#/components/schemas/textemotion_detectionEmotionDetectionDataClass", "default": null } }, "title": "textemotion_detectionResponseModel", "type": "object" }, "textentity_sentimentEntitySentimentDataClass": { "properties": { "items": { "items": { "$ref": "#/components/schemas/Entity" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "items", "status" ], "title": "textentity_sentimentEntitySentimentDataClass", "type": "object" }, "textentity_sentimentEntitySentimentRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "minLength": 1, "description": "Text to analyze" }, "language": { "type": "string", "nullable": true, "description": "Language code for the language the input text is written in (eg: en, fr)." } }, "required": [ "providers", "text" ] }, "textentity_sentimentResponseModel": { "properties": { "google": { "$ref": "#/components/schemas/textentity_sentimentEntitySentimentDataClass", "default": null }, "amazon": { "$ref": "#/components/schemas/textentity_sentimentEntitySentimentDataClass", "default": null } }, "title": "textentity_sentimentResponseModel", "type": "object" }, "textkeyword_extractionKeywordExtractionDataClass": { "properties": { "items": { "items": { "$ref": "#/components/schemas/InfosKeywordExtractionDataClass" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "status" ], "title": "textkeyword_extractionKeywordExtractionDataClass", "type": "object" }, "textkeyword_extractionResponseModel": { "properties": { "xai": { "$ref": "#/components/schemas/textkeyword_extractionKeywordExtractionDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textkeyword_extractionKeywordExtractionDataClass", "default": null }, "oneai": { "$ref": "#/components/schemas/textkeyword_extractionKeywordExtractionDataClass", "default": null }, "microsoft": { "$ref": "#/components/schemas/textkeyword_extractionKeywordExtractionDataClass", "default": null }, "amazon": { "$ref": "#/components/schemas/textkeyword_extractionKeywordExtractionDataClass", "default": null }, "emvista": { "$ref": "#/components/schemas/textkeyword_extractionKeywordExtractionDataClass", "default": null }, "tenstorrent": { "$ref": "#/components/schemas/textkeyword_extractionKeywordExtractionDataClass", "default": null }, "corticalio": { "$ref": "#/components/schemas/textkeyword_extractionKeywordExtractionDataClass", "default": null } }, "title": "textkeyword_extractionResponseModel", "type": "object" }, "textmoderationModerationDataClass": { "properties": { "nsfw_likelihood": { "title": "Nsfw Likelihood", "type": "integer" }, "items": { "items": { "$ref": "#/components/schemas/TextModerationItem" }, "title": "Items", "type": "array" }, "nsfw_likelihood_score": { "title": "Nsfw Likelihood Score", "type": "integer" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "nsfw_likelihood", "nsfw_likelihood_score", "status" ], "title": "textmoderationModerationDataClass", "type": "object" }, "textmoderationResponseModel": { "properties": { "microsoft": { "$ref": "#/components/schemas/textmoderationModerationDataClass", "default": null }, "google": { "$ref": "#/components/schemas/textmoderationModerationDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textmoderationModerationDataClass", "default": null } }, "title": "textmoderationResponseModel", "type": "object" }, "textnamed_entity_recognitionNamedEntityRecognitionDataClass": { "properties": { "items": { "items": { "$ref": "#/components/schemas/InfosNamedEntityRecognitionDataClass" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "status" ], "title": "textnamed_entity_recognitionNamedEntityRecognitionDataClass", "type": "object" }, "textnamed_entity_recognitionResponseModel": { "properties": { "xai": { "$ref": "#/components/schemas/textnamed_entity_recognitionNamedEntityRecognitionDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textnamed_entity_recognitionNamedEntityRecognitionDataClass", "default": null }, "oneai": { "$ref": "#/components/schemas/textnamed_entity_recognitionNamedEntityRecognitionDataClass", "default": null }, "microsoft": { "$ref": "#/components/schemas/textnamed_entity_recognitionNamedEntityRecognitionDataClass", "default": null }, "amazon": { "$ref": "#/components/schemas/textnamed_entity_recognitionNamedEntityRecognitionDataClass", "default": null }, "google": { "$ref": "#/components/schemas/textnamed_entity_recognitionNamedEntityRecognitionDataClass", "default": null }, "tenstorrent": { "$ref": "#/components/schemas/textnamed_entity_recognitionNamedEntityRecognitionDataClass", "default": null } }, "title": "textnamed_entity_recognitionResponseModel", "type": "object" }, "textplagia_detectionPlagiaDetectionDataClass": { "properties": { "plagia_score": { "title": "Plagia Score", "type": "integer" }, "items": { "items": { "$ref": "#/components/schemas/PlagiaDetectionItem" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "plagia_score", "status" ], "title": "textplagia_detectionPlagiaDetectionDataClass", "type": "object" }, "textplagia_detectionPlagiaDetectionRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "minLength": 1, "description": "A text content on which a plagiarism detection analysis will be run" }, "title": { "type": "string", "nullable": true, "default": "", "description": "Content title" } }, "required": [ "providers", "text" ] }, "textplagia_detectionResponseModel": { "properties": { "originalityai": { "$ref": "#/components/schemas/textplagia_detectionPlagiaDetectionDataClass", "default": null }, "winstonai": { "$ref": "#/components/schemas/textplagia_detectionPlagiaDetectionDataClass", "default": null } }, "title": "textplagia_detectionResponseModel", "type": "object" }, "textprompt_optimizationPromptOptimizationDataClass": { "properties": { "missing_information": { "title": "Missing Information", "type": "string" }, "items": { "items": { "$ref": "#/components/schemas/PromptDataClass" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "missing_information", "status" ], "title": "textprompt_optimizationPromptOptimizationDataClass", "type": "object" }, "textprompt_optimizationPromptOptimizationRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "minLength": 1, "description": "Description of the desired prompt." }, "target_provider": { "type": "string", "minLength": 1, "description": "Select the provider for the prompt optimization" } }, "required": [ "providers", "target_provider", "text" ] }, "textprompt_optimizationResponseModel": { "properties": { "openai": { "$ref": "#/components/schemas/textprompt_optimizationPromptOptimizationDataClass", "default": null } }, "title": "textprompt_optimizationResponseModel", "type": "object" }, "textsentiment_analysisResponseModel": { "properties": { "sapling": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "xai": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "ibm": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "oneai": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "microsoft": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "amazon": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "emvista": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "google": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null }, "tenstorrent": { "$ref": "#/components/schemas/textsentiment_analysisSentimentAnalysisDataClass", "default": null } }, "title": "textsentiment_analysisResponseModel", "type": "object" }, "textsentiment_analysisSentimentAnalysisDataClass": { "properties": { "general_sentiment": { "allOf": [ { "$ref": "#/components/schemas/GeneralSentimentEnum" } ], "title": "General Sentiment" }, "general_sentiment_rate": { "maximum": 1, "minimum": 0, "title": "General Sentiment Rate", "type": "integer" }, "items": { "items": { "$ref": "#/components/schemas/SegmentSentimentAnalysisDataClass" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "general_sentiment", "general_sentiment_rate", "status" ], "title": "textsentiment_analysisSentimentAnalysisDataClass", "type": "object" }, "textspell_checkResponseModel": { "properties": { "sapling": { "$ref": "#/components/schemas/textspell_checkSpellCheckDataClass", "default": null }, "xai": { "$ref": "#/components/schemas/textspell_checkSpellCheckDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textspell_checkSpellCheckDataClass", "default": null }, "microsoft": { "$ref": "#/components/schemas/textspell_checkSpellCheckDataClass", "default": null }, "prowritingaid": { "$ref": "#/components/schemas/textspell_checkSpellCheckDataClass", "default": null }, "cohere": { "$ref": "#/components/schemas/textspell_checkSpellCheckDataClass", "default": null } }, "title": "textspell_checkResponseModel", "type": "object" }, "textspell_checkSpellCheckDataClass": { "properties": { "text": { "title": "Text", "type": "string" }, "items": { "items": { "$ref": "#/components/schemas/SpellCheckItem" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "text", "status" ], "title": "textspell_checkSpellCheckDataClass", "type": "object" }, "textspell_checkSpellCheckRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "minLength": 1, "description": "Text to analyze" }, "language": { "type": "string", "nullable": true, "description": "Language code for the language the input text is written in (eg: en, fr)." } }, "required": [ "providers", "text" ] }, "textsummarizeResponseModel": { "properties": { "xai": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "anthropic": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "oneai": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "alephalpha": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "microsoft": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "writesonic": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "cohere": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "meaningcloud": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null }, "emvista": { "$ref": "#/components/schemas/textsummarizeSummarizeDataClass", "default": null } }, "title": "textsummarizeResponseModel", "type": "object" }, "textsummarizeSummarizeDataClass": { "properties": { "result": { "title": "Result", "type": "string" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "result", "status" ], "title": "textsummarizeSummarizeDataClass", "type": "object" }, "textsummarizeSummarizeRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "minLength": 1, "description": "Text to analyze" }, "language": { "type": "string", "nullable": true, "description": "Language code for the language the input text is written in (eg: en, fr)." }, "output_sentences": { "type": "integer", "minimum": 1, "default": 1 } }, "required": [ "providers", "text" ] }, "texttopic_extractionResponseModel": { "properties": { "xai": { "$ref": "#/components/schemas/texttopic_extractionTopicExtractionDataClass", "default": null }, "google": { "$ref": "#/components/schemas/texttopic_extractionTopicExtractionDataClass", "default": null }, "openai": { "$ref": "#/components/schemas/texttopic_extractionTopicExtractionDataClass", "default": null }, "tenstorrent": { "$ref": "#/components/schemas/texttopic_extractionTopicExtractionDataClass", "default": null } }, "title": "texttopic_extractionResponseModel", "type": "object" }, "texttopic_extractionTopicExtractionDataClass": { "properties": { "items": { "items": { "$ref": "#/components/schemas/ExtractedTopic" }, "title": "Items", "type": "array" }, "original_response": { "default": null, "description": "original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request", "title": "Original Response" }, "status": { "allOf": [ { "$ref": "#/components/schemas/StatusEnum" } ], "title": "Status" } }, "required": [ "status" ], "title": "texttopic_extractionTopicExtractionDataClass", "type": "object" }, "texttopic_extractiontextanonymizationtextmoderationtextnamed_entity_recognitiontextkeyword_extractiontextsyntax_analysistextsentiment_analysisTextAnalysisRequest": { "type": "object", "properties": { "settings": { "type": "string", "default": {}, "description": "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n " }, "providers": { "type": "array", "items": { "type": "string", "minLength": 1 }, "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.
Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)" }, "fallback_providers": { "type": "array", "items": { "type": "string" }, "default": [], "description": "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n ", "maxItems": 5 }, "response_as_dict": { "type": "boolean", "default": true, "description": "Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
\n ``` {\"google\" : { \"status\": \"success\", ... }, } ```
\n When set to **false** the response structure is a list of response objects :
\n ``` [{\"status\": \"success\", \"provider\": \"google\" ... }, ] ```.
\n " }, "attributes_as_list": { "type": "boolean", "default": false, "description": "Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
\n ```{'items': [{\"attribute_1\": \"x1\",\"attribute_2\": \"y2\"}, ... ]}```
\n When it is set to **true**, the response contains an object with each attribute as a list :
\n ```{ \"attribute_1\": [\"x1\",\"x2\", ...], \"attribute_2\": [y1, y2, ...]}``` " }, "show_base_64": { "type": "boolean", "default": true }, "show_original_response": { "type": "boolean", "default": false, "description": "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object." }, "text": { "type": "string", "minLength": 1, "description": "Text to analyze" }, "language": { "type": "string", "nullable": true, "description": "Language code for the language the input text is written in (eg: en, fr)." } }, "required": [ "providers", "text" ] } }, "securitySchemes": { "FeatureApiAuth": { "type": "http", "scheme": "bearer", "bearerFormat": "JWT" } } }, "servers": [ { "url": "https://api.edenai.run/v2" } ] }