--- updatedAt: 2026-08-06T10:00:20.000Z --- # Request response from the model Invokes inference using the model chat parameters. # OpenAPI definition ```json { "openapi": "3.1.0", "info": { "title": "NVIDIA NIM API for nvidia/nemotron-3-nano-omni-30b-a3b-reasoning", "description": "The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/nvidia-nemotron-3-nano-omni-30b-a3b-reasoning for more details.", "version": "1.0.0", "termsOfService": "https://nvidia.com/legal/terms-of-use", "contact": { "name": "NVIDIA Support", "url": "https://help.nvidia.com/" }, "license": { "name": "NVIDIA Open Model License", "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/" } }, "servers": [ { "url": "https://integrate.api.nvidia.com/v1" } ], "tags": [ { "name": "Multimodal API", "description": "This API performs inference using text, image, video, and audio understanding models" } ], "paths": { "/chat/completions": { "post": { "tags": [ "Multimodal API" ], "summary": "Request response from the model", "description": "Invokes inference using the model chat parameters.", "operationId": "invokeFunction", "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/NIMLLMChatCompletionRequest" } } }, "required": true }, "responses": { "200": { "description": "Invocation is fulfilled", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ChatCompletionResponse" } }, "text/event-stream": { "schema": { "$ref": "#/components/schemas/ChatCompletionStreamResponse" } } } }, "202": { "description": "Result is pending. Client should poll using the requestId.\n", "content": { "application/json": { "example": {}, "schema": {} } }, "headers": { "NVCF-REQID": { "description": "requestId required for pooling", "schema": { "type": "string", "format": "uuid", "maxLength": 36 } }, "NVCF-STATUS": { "description": "Invocation status", "schema": { "type": "string", "format": "^[a-zA-Z-]{1,64}$", "maxLength": 64 } } } }, "422": { "description": "Validation failed, provided entity could not be processed.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" }, "example": { "type": "urn:nvcf-worker-service:problem-details:unprocessable-entity", "title": "Unprocessable Entity", "status": 422, "detail": "string", "instance": "/v2/nvcf/pexec/functions/e598bfc1-b058-41af-869d-556d3c7e1b48", "requestId": "3fa85f64-5717-4562-b3fc-2c963f66afa6" } } } }, "500": { "description": "The invocation ended with an error.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" }, "example": { "type": "urn:nvcf-worker-service:problem-details:internal-server-error", "title": "Internal Server Error", "status": 500, "detail": "string", "instance": "/v2/nvcf/pexec/functions/e598bfc1-b058-41af-869d-556d3c7e1b48", "requestId": "3fa85f64-5717-4562-b3fc-2c963f66afa6" } } } } }, "x-nvai-meta": { "name": "Create chat completion", "returns": "Returns a [chat completion](/docs/api-reference/chat/object) object, or a streamed sequence of [chat completion chunk](/docs/api-reference/chat/streaming) objects if the request is streamed.\n", "path": "create", "examples": [ { "name": "dogs.jpeg", "input": { "text": "What is in this image?", "images": [ "https://assets.ngc.nvidia.com/products/api-catalog/phi-3-5-vision/example1b.jpg" ] }, "output": { "text": "The image showcases a serene landscape featuring a wooden boardwalk that cuts through a vast, grassy area. The boardwalk is the central focus, inviting viewers to imagine strolling through this peaceful environment. The grass surrounding the boardwalk is very tall, giving a sense of immersion in nature. In the distance, a variety of trees can be seen, their varied foliage adding complexity to the scene. The sky is predominantly blue, interspersed with wispy clouds that are likely illuminated by the sun, enhancing the tranquil and inviting nature of the scene. The time of day appears to be late afternoon, inferred from the warm colors in the foliage and the gentle, diffuse lighting." }, "requestJson": "{ \"model\": \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\", \"messages\": [ { \"role\": \"user\", \"content\": [ { \"type\": \"text\", \"text\": \"<%- input.text %>\" }, { \"type\": \"image_url\", \"image_url\": { \"url\": \"<%- input.images[0] %>\" } } ] } ], \"max_tokens\": 65536, \"temperature\": 0.6, \"top_p\": 0.95, \"reasoning_budget\": 16384, \"chat_template_kwargs\": { \"enable_thinking\": true }, \"stream\": true }", "responseJson": "{ \"id\": \"chatcmpl-abc123\", \"object\": \"chat.completion\", \"created\": 1677858242, \"model\": \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\", \"choices\": [ { \"index\": 0, \"message\": { \"role\": \"assistant\", \"content\": \"<%- output.text %>\" }, \"finish_reason\": \"stop\" } ], \"usage\": { \"prompt_tokens\": 256, \"completion_tokens\": 24, \"total_tokens\": 280 } }" }, { "name": "video.mp4", "input": { "text": "Describe what you see in this video.", "videos": [ "https://assets.ngc.nvidia.com/products/api-catalog/nvidia-nemotron-nano-12b-v2-vl/nvidia-studio-itns-wk53-scene-in-omniverse-1280w.mp4" ] }, "output": { "text": "The video displays a 3D model of a small, rustic cabin with a thatched roof, situated in a snowy landscape. The cabin is constructed with wooden planks and has a stone chimney. Surrounding the cabin are various objects such as logs, a wooden table, and a barrel. The ground is covered in snow, and there are trees in the background. The color palette is predominantly white and gray, with the brown of the wooden structures providing contrast. The video also shows a user interface with various tools and settings, indicating that the scene is being edited or manipulated in a 3D modeling software." }, "requestJson": "{ \"model\": \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\", \"messages\": [ { \"role\": \"user\", \"content\": [ { \"type\": \"text\", \"text\": \"<%- input.text %>\" }, { \"type\": \"video_url\", \"video_url\": { \"url\": \"<%- input.videos[0] %>\" } } ] } ], \"max_tokens\": 65536, \"temperature\": 0.6, \"top_p\": 0.95, \"reasoning_budget\": 16384, \"chat_template_kwargs\": { \"enable_thinking\": true }, \"stream\": true }", "responseJson": "{ \"id\": \"chatcmpl-def456\", \"object\": \"chat.completion\", \"created\": 1677858300, \"model\": \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\", \"choices\": [ { \"index\": 0, \"message\": { \"role\": \"assistant\", \"content\": \"<%- output.text %>\" }, \"finish_reason\": \"stop\" } ], \"usage\": { \"prompt_tokens\": 512, \"completion_tokens\": 48, \"total_tokens\": 560 } }" }, { "name": "audio.wav", "input": { "text": "Transcribe the spoken content.", "audios": [ "https://assets.ngc.nvidia.com/products/api-catalog/nemotron-3-nano-omni-reasoning-30b-a3b/voice-sample.wav" ] }, "output": { "text": "Hi there, this is a sample voice recording created for speech synthesis testing. The quick brown fox jumps over the lazy dog. Just a fun way to include every letter of the alphabet. Numbers, like 1, 2, 3, are spoken clearly. Let's see how well this voice captures tone, timing, and natural rhythm. This audio is provided by samplefiles.com." }, "requestJson": "{ \"model\": \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\", \"messages\": [ { \"role\": \"user\", \"content\": [ { \"type\": \"audio_url\", \"audio_url\": { \"url\": \"<%- input.audios[0] %>\" } }, { \"type\": \"text\", \"text\": \"<%- input.text %>\" } ] } ], \"max_tokens\": 65536, \"temperature\": 0.6, \"top_p\": 0.95, \"reasoning_budget\": 16384, \"chat_template_kwargs\": { \"enable_thinking\": true }, \"stream\": true }", "responseJson": "{ \"id\": \"chatcmpl-ghi789\", \"object\": \"chat.completion\", \"created\": 1677858360, \"model\": \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\", \"choices\": [ { \"index\": 0, \"message\": { \"role\": \"assistant\", \"content\": \"<%- output.text %>\" }, \"finish_reason\": \"stop\" } ], \"usage\": { \"prompt_tokens\": 192, \"completion_tokens\": 16, \"total_tokens\": 208 } }" } ], "templates": [ { "title": "No Streaming", "response": "{\n \"choices\": [{\n \"index\": 0,\n \"model\": \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\",\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"...\",\n },\n \"finish_reason\": \"stop\"\n }],\n \"usage\": {\n \"prompt_tokens\": 9,\n \"completion_tokens\": 12,\n \"total_tokens\": 21\n }\n}\n", "requestEjs": { "python": "<%\n // Catalog ejs locals replace JSON with {stringify} only (spread skips non-enumerable parse).\n const deepClone = (value) => {\n if (Array.isArray(value)) return value.map(deepClone);\n if (value && typeof value === \"object\") {\n const out = {};\n for (const key of Object.keys(value)) out[key] = deepClone(value[key]);\n return out;\n }\n return value;\n };\n const mediaConfig = {\"image\": {\"mimeTypes\": {\".png\": \"image/png\", \".jpg\": \"image/jpeg\", \".jpeg\": \"image/jpeg\", \".webp\": \"image/webp\"}}, \"video\": {\"mimeTypes\": {\".mp4\": \"video/mp4\", \".mov\": \"video/quicktime\", \".webm\": \"video/webm\"}}, \"audio\": {\"mimeTypes\": {\".wav\": \"audio/wav\", \".mp3\": \"audio/mpeg\"}, \"defaultFormat\": \"wav\"}};\n const stripNulls = (value) => {\n if (Array.isArray(value)) return value.map(stripNulls);\n if (value && typeof value === \"object\") {\n const out = {};\n for (const [key, item] of Object.entries(value)) {\n if (item === null || item === undefined) continue;\n out[key] = stripNulls(item);\n }\n return out;\n }\n return value;\n };\n const payloadForSnippet = stripNulls(deepClone(request || {}));\n const hasStream = payloadForSnippet.stream !== undefined && payloadForSnippet.stream !== null;\n const pyStream = payloadForSnippet.stream ? \"True\" : \"False\";\n if (hasStream) payloadForSnippet.stream = \"__STREAM__\";\n\n const hasMediaKind = (kind) => Object.keys(mediaConfig[kind].mimeTypes || {}).length > 0;\n const isInlineDataUrl = (url, kind) => (\n typeof url === \"string\" && new RegExp(`^data:${kind}/[^;]+;base64,`, \"i\").test(url)\n );\n\n const mediaUrl = (part, key) => {\n const slot = part?.[key];\n return typeof slot === \"string\" ? slot : slot?.url;\n };\n const setMediaUrl = (part, key, url) => {\n if (typeof part[key] === \"string\") part[key] = url;\n else {\n if (!part[key] || typeof part[key] !== \"object\") part[key] = {};\n part[key].url = url;\n }\n };\n\n let imageCount = 0;\n let videoCount = 0;\n let audioCount = 0;\n\n (payloadForSnippet.messages || []).forEach((message) => {\n if (!Array.isArray(message?.content)) return;\n message.content.forEach((part) => {\n const imageUrl = mediaUrl(part, \"image_url\");\n const videoUrl = mediaUrl(part, \"video_url\");\n const audioUrl = mediaUrl(part, \"audio_url\");\n // File helpers only for inline base64; http(s) URLs stay as-is in the payload.\n if (hasMediaKind(\"image\") && part?.type === \"image_url\" && isInlineDataUrl(imageUrl, \"image\")) {\n setMediaUrl(part, \"image_url\", `__IMAGE_DATA_URL_${imageCount}__`);\n imageCount += 1;\n } else if (hasMediaKind(\"video\") && part?.type === \"video_url\" && isInlineDataUrl(videoUrl, \"video\")) {\n setMediaUrl(part, \"video_url\", `__VIDEO_DATA_URL_${videoCount}__`);\n videoCount += 1;\n } else if (hasMediaKind(\"audio\") && part?.type === \"audio_url\" && isInlineDataUrl(audioUrl, \"audio\")) {\n setMediaUrl(part, \"audio_url\", `__AUDIO_DATA_URL_${audioCount}__`);\n audioCount += 1;\n } else if (hasMediaKind(\"audio\") && part?.type === \"input_audio\" && part.input_audio?.data) {\n part.input_audio.data = `__AUDIO_B64_${audioCount}__`;\n part.input_audio.format = part.input_audio.format || mediaConfig.audio.defaultFormat;\n audioCount += 1;\n }\n });\n });\n\n const pyRepr = (value, level = 0) => {\n if (value === null) return \"None\";\n if (typeof value === \"boolean\") return value ? \"True\" : \"False\";\n if (typeof value === \"number\") return String(value);\n if (typeof value === \"string\") return JSON.stringify(value);\n const pad = \" \".repeat(level);\n const innerPad = \" \".repeat(level + 2);\n if (Array.isArray(value)) {\n if (value.length === 0) return \"[]\";\n return \"[\\n\" + value.map((item) => `${innerPad}${pyRepr(item, level + 2)}`).join(\",\\n\") + \"\\n\" + pad + \"]\";\n }\n if (typeof value === \"object\") {\n const entries = Object.entries(value);\n if (entries.length === 0) return \"{}\";\n return \"{\\n\" + entries.map(([key, item]) => `${innerPad}${JSON.stringify(key)}: ${pyRepr(item, level + 2)}`).join(\",\\n\") + \"\\n\" + pad + \"}\";\n }\n return \"None\";\n };\n\n const replaceAll = (s, a, b) => s.split(a).join(b);\n let renderedPayload = pyRepr(payloadForSnippet);\n if (hasStream) renderedPayload = replaceAll(renderedPayload, '\"__STREAM__\"', \"stream\");\n for (let i = 0; i < imageCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__IMAGE_DATA_URL_${i}__\"`, `image_data_urls[${i}]`);\n }\n for (let i = 0; i < videoCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__VIDEO_DATA_URL_${i}__\"`, `video_data_urls[${i}]`);\n }\n for (let i = 0; i < audioCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__AUDIO_DATA_URL_${i}__\"`, `audio_data_urls[${i}]`);\n renderedPayload = replaceAll(renderedPayload, `\"__AUDIO_B64_${i}__\"`, `audio_b64s[${i}]`);\n }\n\n // Either file helpers XOR inline base64 - never emit unused helpers.\n const usesFileHelpers =\n imageCount + videoCount + audioCount > 0 &&\n [...Array(imageCount).keys()].every((i) => renderedPayload.includes(`image_data_urls[${i}]`)) &&\n [...Array(videoCount).keys()].every((i) => renderedPayload.includes(`video_data_urls[${i}]`)) &&\n [...Array(audioCount).keys()].every(\n (i) => renderedPayload.includes(`audio_data_urls[${i}]`) || renderedPayload.includes(`audio_b64s[${i}]`)\n );\n if (!usesFileHelpers) {\n imageCount = 0;\n videoCount = 0;\n audioCount = 0;\n const restored = stripNulls(deepClone(request || {}));\n const restoredStream = restored.stream !== undefined && restored.stream !== null;\n if (restoredStream) restored.stream = \"__STREAM__\";\n renderedPayload = pyRepr(restored);\n if (restoredStream) renderedPayload = replaceAll(renderedPayload, '\"__STREAM__\"', \"stream\");\n }\n const hasMedia = usesFileHelpers;\n const mimePy = (mimes) =>\n \"{\" + Object.entries(mimes).map(([k, v]) => `${JSON.stringify(k)}: ${JSON.stringify(v)}`).join(\", \") + \"}\";\n%>\n<% if (hasMedia) { %>import base64\nfrom pathlib import Path\n<% } %>import requests\n\ninvoke_url = \"https://integrate.api.nvidia.com/v1/chat/completions\"\nstream = <%- pyStream %>\n<% if (hasMedia) { %>\ndef media_data_url(kind, index, mime_types):\n for suffix, mime in mime_types.items():\n path = Path(f\"{kind}_{index}{suffix}\")\n if path.exists():\n return f\"data:{mime};base64,{base64.b64encode(path.read_bytes()).decode()}\"\n raise FileNotFoundError(f\"Expected {kind}_{index} with suffix in {list(mime_types)}\")\n<% if (imageCount > 0) { %>\nimage_data_urls = [media_data_url(\"image\", i, <%- mimePy(mediaConfig.image.mimeTypes) %>) for i in <%- imageCount === 1 ? \"[1]\" : (\"range(1, \" + (imageCount + 1) + \")\") %>]\n<% } %><% if (videoCount > 0) { %>\nvideo_data_urls = [media_data_url(\"video\", i, <%- mimePy(mediaConfig.video.mimeTypes) %>) for i in <%- videoCount === 1 ? \"[1]\" : (\"range(1, \" + (videoCount + 1) + \")\") %>]\n<% } %><% if (audioCount > 0) { %>\naudio_data_urls = [media_data_url(\"audio\", i, <%- mimePy(mediaConfig.audio.mimeTypes) %>) for i in <%- audioCount === 1 ? \"[1]\" : (\"range(1, \" + (audioCount + 1) + \")\") %>]\naudio_b64s = [url.split(\",\", 1)[1] for url in audio_data_urls]\n<% } %><% } %>\nheaders = {\n \"Authorization\": \"Bearer $NVIDIA_API_KEY\",\n \"Accept\": \"text/event-stream\" if stream else \"application/json\",\n}\n\npayload = <%- renderedPayload %>\n\nresponse = requests.post(invoke_url, headers=headers, json=payload, stream=stream)\nif stream:\n for line in response.iter_lines():\n if line:\n print(line.decode(\"utf-8\"))\nelse:\n print(response.json())\n", "langChain": "<%\n const deepClone = (value) => {\n if (Array.isArray(value)) return value.map(deepClone);\n if (value && typeof value === \"object\") {\n const out = {};\n for (const key of Object.keys(value)) out[key] = deepClone(value[key]);\n return out;\n }\n return value;\n };\n const mediaConfig = {\"image\": {\"mimeTypes\": {\".png\": \"image/png\", \".jpg\": \"image/jpeg\", \".jpeg\": \"image/jpeg\", \".webp\": \"image/webp\"}}, \"video\": {\"mimeTypes\": {\".mp4\": \"video/mp4\", \".mov\": \"video/quicktime\", \".webm\": \"video/webm\"}}, \"audio\": {\"mimeTypes\": {\".wav\": \"audio/wav\", \".mp3\": \"audio/mpeg\"}, \"defaultFormat\": \"wav\"}};\n const payloadForSnippet = deepClone(request || {});\n const messagesForSnippet = deepClone(payloadForSnippet.messages || []);\n const hasMediaKind = (kind) => Object.keys(mediaConfig[kind].mimeTypes || {}).length > 0;\n const isInlineDataUrl = (url, kind) => (\n typeof url === \"string\" && new RegExp(`^data:${kind}/[^;]+;base64,`, \"i\").test(url)\n );\n\n const mediaUrl = (part, key) => {\n const slot = part?.[key];\n return typeof slot === \"string\" ? slot : slot?.url;\n };\n const setMediaUrl = (part, key, url) => {\n if (typeof part[key] === \"string\") part[key] = url;\n else {\n if (!part[key] || typeof part[key] !== \"object\") part[key] = {};\n part[key].url = url;\n }\n };\n\n let imageCount = 0;\n let hasUnsupportedMedia = false;\n messagesForSnippet.forEach((message) => {\n if (!Array.isArray(message?.content)) return;\n message.content.forEach((part) => {\n const imageUrl = mediaUrl(part, \"image_url\");\n if (hasMediaKind(\"image\") && part?.type === \"image_url\" && isInlineDataUrl(imageUrl, \"image\")) {\n setMediaUrl(part, \"image_url\", `__IMAGE_DATA_URL_${imageCount}__`);\n imageCount += 1;\n } else if (part?.type === \"video_url\" && isInlineDataUrl(mediaUrl(part, \"video_url\"), \"video\")) {\n // LangChain cannot ship local file helpers for video; reject inline only.\n hasUnsupportedMedia = true;\n setMediaUrl(part, \"video_url\", \"__UNSUPPORTED_MEDIA_DATA__\");\n } else if (part?.type === \"audio_url\" && isInlineDataUrl(mediaUrl(part, \"audio_url\"), \"audio\")) {\n hasUnsupportedMedia = true;\n setMediaUrl(part, \"audio_url\", \"__UNSUPPORTED_MEDIA_DATA__\");\n } else if (part?.type === \"input_audio\" && part.input_audio?.data) {\n hasUnsupportedMedia = true;\n part.input_audio.data = \"__UNSUPPORTED_MEDIA_DATA__\";\n }\n });\n });\n\n const pyRepr = (value, level = 0) => {\n if (value === null) return \"None\";\n if (typeof value === \"boolean\") return value ? \"True\" : \"False\";\n if (typeof value === \"number\") return String(value);\n if (typeof value === \"string\") return JSON.stringify(value);\n const pad = \" \".repeat(level);\n const innerPad = \" \".repeat(level + 2);\n if (Array.isArray(value)) {\n if (value.length === 0) return \"[]\";\n return \"[\\n\" + value.map((item) => `${innerPad}${pyRepr(item, level + 2)},`).join(\"\\n\") + \"\\n\" + pad + \"]\";\n }\n if (typeof value === \"object\") {\n const entries = Object.entries(value);\n if (entries.length === 0) return \"{}\";\n return \"{\\n\" + entries.map(([key, item]) => `${innerPad}${JSON.stringify(key)}: ${pyRepr(item, level + 2)},`).join(\"\\n\") + \"\\n\" + pad + \"}\";\n }\n return \"None\";\n };\n\n const replaceAll = (s, a, b) => s.split(a).join(b);\n let renderedMessages = pyRepr(messagesForSnippet);\n for (let i = 0; i < imageCount; i++) {\n renderedMessages = replaceAll(renderedMessages, `\"__IMAGE_DATA_URL_${i}__\"`, `image_data_urls[${i}]`);\n }\n // Either file helpers XOR inline base64 - never emit unused helpers.\n const usesFileHelpers =\n imageCount > 0 &&\n [...Array(imageCount).keys()].every((i) => renderedMessages.includes(`image_data_urls[${i}]`));\n if (!usesFileHelpers) {\n imageCount = 0;\n renderedMessages = pyRepr(deepClone((request || {}).messages || []));\n }\n%>\nfrom langchain_nvidia_ai_endpoints import ChatNVIDIA\n<% if (imageCount > 0) { %>from pathlib import Path\nimport base64\n<% } %>\n\nclient = ChatNVIDIA(\n model=\"<%- request.model %>\",\n api_key=\"$NVIDIA_API_KEY\",\n temperature=<%- request.temperature %>,\n top_p=<%- request.top_p %>,\n max_completion_tokens=<%- request.max_tokens %>,\n)\n<% if (imageCount > 0) { %>\ndef find_image_path(index):\n image_mime_types = <%- pyRepr(mediaConfig.image.mimeTypes) %>\n for suffix in image_mime_types:\n path = Path(f\"image_{index}{suffix}\")\n if path.exists():\n return path\n expected = \"/\".join(f\".{suffix.lstrip('.')}\" for suffix in image_mime_types)\n raise FileNotFoundError(f\"Expected image_{index}{expected}\")\n\ndef read_image_data_url(path):\n image_mime_types = <%- pyRepr(mediaConfig.image.mimeTypes) %>\n with open(path, \"rb\") as f:\n image_b64 = base64.b64encode(f.read()).decode()\n return f\"data:{image_mime_types[path.suffix.lower()]};base64,{image_b64}\"\n\nimage_data_urls = [read_image_data_url(find_image_path(i)) for i in range(1, <%- imageCount + 1 %>)]\n<% } %><% if (hasUnsupportedMedia) { %>\nraise ValueError(\"LangChain snippet currently supports text and image input. Use Python, Node.js, or Shell for video or audio input.\")\n<% } %>\nlc_messages = <%- renderedMessages %>\n<% if (request.stream) { %>\nfor chunk in client.stream(lc_messages<%= request.chat_template_kwargs ? ', chat_template_kwargs=' + pyRepr(request.chat_template_kwargs) : '' %>):\n if chunk.additional_kwargs and \"reasoning_content\" in chunk.additional_kwargs:\n print(chunk.additional_kwargs[\"reasoning_content\"], end=\"\")\n print(chunk.content, end=\"\")\n<% } else { %>\nresponse = client.invoke(lc_messages<%= request.chat_template_kwargs ? ', chat_template_kwargs=' + pyRepr(request.chat_template_kwargs) : '' %>)\nif response.additional_kwargs and \"reasoning_content\" in response.additional_kwargs:\n print(response.additional_kwargs[\"reasoning_content\"])\nprint(response.content)\n<% } %>\n", "node.js": "<%\n const deepClone = (value) => {\n if (Array.isArray(value)) return value.map(deepClone);\n if (value && typeof value === \"object\") {\n const out = {};\n for (const key of Object.keys(value)) out[key] = deepClone(value[key]);\n return out;\n }\n return value;\n };\n const mediaConfig = {\"image\": {\"mimeTypes\": {\".png\": \"image/png\", \".jpg\": \"image/jpeg\", \".jpeg\": \"image/jpeg\", \".webp\": \"image/webp\"}}, \"video\": {\"mimeTypes\": {\".mp4\": \"video/mp4\", \".mov\": \"video/quicktime\", \".webm\": \"video/webm\"}}, \"audio\": {\"mimeTypes\": {\".wav\": \"audio/wav\", \".mp3\": \"audio/mpeg\"}, \"defaultFormat\": \"wav\"}};\n const payloadForSnippet = deepClone(request || {});\n const hasStream = payloadForSnippet.stream !== undefined && payloadForSnippet.stream !== null;\n if (hasStream) payloadForSnippet.stream = \"__STREAM__\";\n\n const hasMediaKind = (kind) => Object.keys(mediaConfig[kind].mimeTypes || {}).length > 0;\n const isInlineDataUrl = (url, kind) => (\n typeof url === \"string\" && new RegExp(`^data:${kind}/[^;]+;base64,`, \"i\").test(url)\n );\n\n let imageCount = 0;\n let videoCount = 0;\n let audioCount = 0;\n (payloadForSnippet.messages || []).forEach((message) => {\n if (!Array.isArray(message?.content)) return;\n message.content.forEach((part) => {\n if (hasMediaKind(\"image\") && part?.type === \"image_url\" && isInlineDataUrl(part.image_url?.url, \"image\")) {\n part.image_url.url = `__IMAGE_DATA_URL_${imageCount}__`;\n imageCount += 1;\n } else if (hasMediaKind(\"video\") && part?.type === \"video_url\" && isInlineDataUrl(part.video_url?.url, \"video\")) {\n part.video_url.url = `__VIDEO_DATA_URL_${videoCount}__`;\n videoCount += 1;\n } else if (hasMediaKind(\"audio\") && part?.type === \"audio_url\" && isInlineDataUrl(part.audio_url?.url, \"audio\")) {\n part.audio_url.url = `__AUDIO_DATA_URL_${audioCount}__`;\n audioCount += 1;\n } else if (hasMediaKind(\"audio\") && part?.type === \"input_audio\" && part.input_audio?.data) {\n part.input_audio.data = `__AUDIO_B64_${audioCount}__`;\n part.input_audio.format = part.input_audio.format || mediaConfig.audio.defaultFormat;\n audioCount += 1;\n }\n });\n });\n\n const replaceAll = (s, a, b) => s.split(a).join(b);\n let renderedPayload = JSON.stringify(payloadForSnippet, null, 2);\n if (hasStream) renderedPayload = replaceAll(renderedPayload, '\"__STREAM__\"', \"stream\");\n for (let i = 0; i < imageCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__IMAGE_DATA_URL_${i}__\"`, `imageDataUrls[${i}]`);\n }\n for (let i = 0; i < videoCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__VIDEO_DATA_URL_${i}__\"`, `videoDataUrls[${i}]`);\n }\n for (let i = 0; i < audioCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__AUDIO_DATA_URL_${i}__\"`, `audioDataUrls[${i}]`);\n renderedPayload = replaceAll(renderedPayload, `\"__AUDIO_B64_${i}__\"`, `audioB64s[${i}]`);\n }\n // Either file helpers XOR inline base64 - never emit unused helpers.\n const usesFileHelpers =\n imageCount + videoCount + audioCount > 0 &&\n [...Array(imageCount).keys()].every((i) => renderedPayload.includes(`imageDataUrls[${i}]`)) &&\n [...Array(videoCount).keys()].every((i) => renderedPayload.includes(`videoDataUrls[${i}]`)) &&\n [...Array(audioCount).keys()].every(\n (i) => renderedPayload.includes(`audioDataUrls[${i}]`) || renderedPayload.includes(`audioB64s[${i}]`)\n );\n if (!usesFileHelpers) {\n imageCount = 0;\n videoCount = 0;\n audioCount = 0;\n const restored = deepClone(request || {});\n const restoredStream = restored.stream !== undefined && restored.stream !== null;\n if (restoredStream) restored.stream = \"__STREAM__\";\n renderedPayload = JSON.stringify(restored, null, 2);\n if (restoredStream) renderedPayload = replaceAll(renderedPayload, '\"__STREAM__\"', \"stream\");\n }\n const hasMedia = usesFileHelpers;\n%>\nimport axios from 'axios';\n<% if (hasMedia) { %>import { existsSync } from 'node:fs';\nimport { readFile } from 'node:fs/promises';\nimport { extname } from 'node:path';\n<% } %>\n\nconst invokeUrl = \"https://integrate.api.nvidia.com/v1/chat/completions\";\nconst stream = <%- request.stream %>;\n\nconst headers = {\n \"Authorization\": \"Bearer $NVIDIA_API_KEY\",\n \"Accept\": stream ? \"text/event-stream\" : \"application/json\"\n};\n<% if (hasMedia) { %>\nfunction findMediaPath(kind, index, mimeTypes) {\n for (const suffix of Object.keys(mimeTypes)) {\n const path = `${kind}_${index}${suffix}`;\n if (existsSync(path)) {\n return path;\n }\n }\n const expected = Object.keys(mimeTypes).join(\"/\");\n throw new Error(`Expected ${kind}_${index}${expected}`);\n}\n\nasync function readMediaB64(path) {\n return Buffer.from(await readFile(path)).toString('base64');\n}\n\nfunction mediaDataUrl(path, mediaB64, mimeTypes) {\n return `data:${mimeTypes[extname(path).toLowerCase()]};base64,${mediaB64}`;\n}\n<% } %>\nasync function main() {\n<% if (imageCount > 0) { %> const imageMimeTypes = <%- JSON.stringify(mediaConfig.image.mimeTypes, null, 2) %>;\n const imagePaths = [<% for (let i = 1; i <= imageCount; i++) { %>findMediaPath(\"image\", <%- i %>, imageMimeTypes)<%- i < imageCount ? \", \" : \"\" %><% } %>];\n const imageB64s = await Promise.all(imagePaths.map(readMediaB64));\n const imageDataUrls = imagePaths.map((path, index) => mediaDataUrl(path, imageB64s[index], imageMimeTypes));\n<% } %><% if (videoCount > 0) { %> const videoMimeTypes = <%- JSON.stringify(mediaConfig.video.mimeTypes, null, 2) %>;\n const videoPaths = [<% for (let i = 1; i <= videoCount; i++) { %>findMediaPath(\"video\", <%- i %>, videoMimeTypes)<%- i < videoCount ? \", \" : \"\" %><% } %>];\n const videoB64s = await Promise.all(videoPaths.map(readMediaB64));\n const videoDataUrls = videoPaths.map((path, index) => mediaDataUrl(path, videoB64s[index], videoMimeTypes));\n<% } %><% if (audioCount > 0) { %> const audioMimeTypes = <%- JSON.stringify(mediaConfig.audio.mimeTypes, null, 2) %>;\n const audioPaths = [<% for (let i = 1; i <= audioCount; i++) { %>findMediaPath(\"audio\", <%- i %>, audioMimeTypes)<%- i < audioCount ? \", \" : \"\" %><% } %>];\n const audioB64s = await Promise.all(audioPaths.map(readMediaB64));\n const audioDataUrls = audioPaths.map((path, index) => mediaDataUrl(path, audioB64s[index], audioMimeTypes));\n<% } %> const payload = <%- renderedPayload %>;\n\n const response = await axios.post(invokeUrl, payload, {\n headers: headers,\n responseType: stream ? 'stream' : 'json'\n });\n\n if (stream) {\n response.data.on('data', (chunk) => {\n console.log(chunk.toString());\n });\n } else {\n console.log(JSON.stringify(response.data));\n }\n}\n\nmain().catch(error => {\n if (error.response) {\n console.error(`HTTP ${error.response.status}`);\n if (error.response.data?.on) {\n error.response.data.on('data', (chunk) => console.error(chunk.toString()));\n } else {\n console.error(error.response.data);\n }\n } else {\n console.error(error);\n }\n});\n", "curl": "<%\n const deepClone = (value) => {\n if (Array.isArray(value)) return value.map(deepClone);\n if (value && typeof value === \"object\") {\n const out = {};\n for (const key of Object.keys(value)) out[key] = deepClone(value[key]);\n return out;\n }\n return value;\n };\n const mediaConfig = {\"image\": {\"mimeTypes\": {\".png\": \"image/png\", \".jpg\": \"image/jpeg\", \".jpeg\": \"image/jpeg\", \".webp\": \"image/webp\"}}, \"video\": {\"mimeTypes\": {\".mp4\": \"video/mp4\", \".mov\": \"video/quicktime\", \".webm\": \"video/webm\"}}, \"audio\": {\"mimeTypes\": {\".wav\": \"audio/wav\", \".mp3\": \"audio/mpeg\"}, \"defaultFormat\": \"wav\"}};\n const payloadForSnippet = deepClone(request || {});\n const hasMediaKind = (kind) => Object.keys(mediaConfig[kind].mimeTypes || {}).length > 0;\n const isInlineDataUrl = (url, kind) => (\n typeof url === \"string\" && new RegExp(`^data:${kind}/[^;]+;base64,`, \"i\").test(url)\n );\n\n let imageCount = 0;\n let videoCount = 0;\n let audioCount = 0;\n (payloadForSnippet.messages || []).forEach((message) => {\n if (!Array.isArray(message?.content)) return;\n message.content.forEach((part) => {\n if (hasMediaKind(\"image\") && part?.type === \"image_url\" && isInlineDataUrl(part.image_url?.url, \"image\")) {\n part.image_url.url = `__IMAGE_DATA_URL_${imageCount}__`;\n imageCount += 1;\n } else if (hasMediaKind(\"video\") && part?.type === \"video_url\" && isInlineDataUrl(part.video_url?.url, \"video\")) {\n part.video_url.url = `__VIDEO_DATA_URL_${videoCount}__`;\n videoCount += 1;\n } else if (hasMediaKind(\"audio\") && part?.type === \"audio_url\" && isInlineDataUrl(part.audio_url?.url, \"audio\")) {\n part.audio_url.url = `__AUDIO_DATA_URL_${audioCount}__`;\n audioCount += 1;\n } else if (hasMediaKind(\"audio\") && part?.type === \"input_audio\" && part.input_audio?.data) {\n part.input_audio.data = `__AUDIO_B64_${audioCount}__`;\n part.input_audio.format = part.input_audio.format || mediaConfig.audio.defaultFormat;\n audioCount += 1;\n }\n });\n });\n\n const replaceAll = (s, a, b) => s.split(a).join(b);\n let renderedPayload = JSON.stringify(payloadForSnippet, null, 2);\n for (let i = 0; i < imageCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__IMAGE_DATA_URL_${i}__\"`, `\"${\"${\"}image_${i + 1}_data_url}\"`);\n }\n for (let i = 0; i < videoCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__VIDEO_DATA_URL_${i}__\"`, `\"${\"${\"}video_${i + 1}_data_url}\"`);\n }\n for (let i = 0; i < audioCount; i++) {\n renderedPayload = replaceAll(renderedPayload, `\"__AUDIO_DATA_URL_${i}__\"`, `\"${\"${\"}audio_${i + 1}_data_url}\"`);\n renderedPayload = replaceAll(renderedPayload, `\"__AUDIO_B64_${i}__\"`, `\"${\"${\"}audio_${i + 1}_b64}\"`);\n }\n // Either file helpers XOR inline base64 - never emit unused helpers.\n const usesFileHelpers =\n imageCount + videoCount + audioCount > 0 &&\n [...Array(imageCount).keys()].every((i) => renderedPayload.includes(`image_${i + 1}_data_url`)) &&\n [...Array(videoCount).keys()].every((i) => renderedPayload.includes(`video_${i + 1}_data_url`)) &&\n [...Array(audioCount).keys()].every(\n (i) => renderedPayload.includes(`audio_${i + 1}_data_url`) || renderedPayload.includes(`audio_${i + 1}_b64`)\n );\n if (!usesFileHelpers) {\n imageCount = 0;\n videoCount = 0;\n audioCount = 0;\n renderedPayload = JSON.stringify(deepClone(request || {}), null, 2);\n }\n const hasMedia = usesFileHelpers;\n%>\nstream=<%- request.stream %>\nif [ \"$stream\" = true ]; then\n accept_header='Accept: text/event-stream'\nelse\n accept_header='Accept: application/json'\nfi\n<% if (hasMedia) { %>\nmime_type_for() {\n case \"$1\" in\n<% for (const [suffix, mimeType] of Object.entries({...mediaConfig.image.mimeTypes, ...mediaConfig.video.mimeTypes, ...mediaConfig.audio.mimeTypes})) { %> *<%- suffix %>) echo \"<%- mimeType %>\" ;;\n<% } %> *) echo \"Unsupported media file: $1\" >&2; return 1 ;;\n esac\n}\n\nfind_media_path() {\n local kind=\"$1\"\n local index=\"$2\"\n shift 2\n local ext path\n for ext in \"$@\"; do\n path=\"${kind}_${index}.${ext}\"\n if [ -f \"$path\" ]; then\n echo \"$path\"\n return 0\n fi\n done\n echo \"Expected ${kind}_${index}.$*\" >&2\n return 1\n}\n\nmedia_b64() {\n base64 < \"$1\" | tr -d '\\n'\n}\n\nmedia_data_url() {\n local path=\"$1\"\n local mime_type media_base64\n mime_type=$(mime_type_for \"$path\") || return 1\n media_base64=$(media_b64 \"$path\") || return 1\n printf 'data:%s;base64,%s' \"$mime_type\" \"$media_base64\"\n}\n<% for (let i = 1; i <= imageCount; i++) { %>\nimage_<%- i %>_path=$(find_media_path image <%- i %><% for (const suffix of Object.keys(mediaConfig.image.mimeTypes)) { %> <%- suffix.slice(1) %><% } %>)\nimage_<%- i %>_data_url=$(media_data_url \"$image_<%- i %>_path\")\n<% } %><% for (let i = 1; i <= videoCount; i++) { %>\nvideo_<%- i %>_path=$(find_media_path video <%- i %><% for (const suffix of Object.keys(mediaConfig.video.mimeTypes)) { %> <%- suffix.slice(1) %><% } %>)\nvideo_<%- i %>_data_url=$(media_data_url \"$video_<%- i %>_path\")\n<% } %><% for (let i = 1; i <= audioCount; i++) { %>\naudio_<%- i %>_path=$(find_media_path audio <%- i %><% for (const suffix of Object.keys(mediaConfig.audio.mimeTypes)) { %> <%- suffix.slice(1) %><% } %>)\naudio_<%- i %>_b64=$(media_b64 \"$audio_<%- i %>_path\")\naudio_<%- i %>_data_url=$(media_data_url \"$audio_<%- i %>_path\")\n<% } %><% } %>\ncat > payload.json <\nJSON\n\ncurl https://integrate.api.nvidia.com/v1/chat/completions \\\n -H \"Authorization: Bearer $NVIDIA_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -H \"$accept_header\" \\\n -d @payload.json" } } ] } } } }, "security": [ { "Token": [] } ], "components": { "securitySchemes": { "Token": { "type": "http", "scheme": "bearer" } }, "schemas": { "ChatCompletionResponse": { "properties": { "id": { "type": "string", "title": "Id" }, "object": { "type": "string", "enum": [ "chat.completion" ], "const": "chat.completion", "title": "Object", "default": "chat.completion" }, "created": { "type": "integer", "title": "Created" }, "model": { "type": "string", "title": "Model" }, "choices": { "items": { "$ref": "#/components/schemas/ChatCompletionResponseChoice" }, "type": "array", "title": "Choices" }, "usage": { "$ref": "#/components/schemas/UsageInfo" } }, "additionalProperties": false, "type": "object", "required": [ "model", "choices", "usage" ], "title": "ChatCompletionResponse" }, "ChatCompletionResponseChoice": { "properties": { "index": { "type": "integer", "title": "Index" }, "message": { "$ref": "#/components/schemas/ChatMessage" }, "finish_reason": { "anyOf": [ { "type": "string", "enum": [ "stop", "length" ] }, { "type": "null" } ], "title": "Finish Reason" }, "stop_reason": { "anyOf": [ { "type": "integer" }, { "type": "string" }, { "type": "null" } ], "title": "Stop Reason" } }, "additionalProperties": false, "type": "object", "required": [ "index", "message" ], "title": "ChatCompletionResponseChoice" }, "ChatCompletionResponseStreamChoice": { "properties": { "index": { "type": "integer", "title": "Index" }, "delta": { "$ref": "#/components/schemas/DeltaMessage" }, "finish_reason": { "anyOf": [ { "type": "string", "enum": [ "stop", "length" ] }, { "type": "null" } ], "title": "Finish Reason" }, "stop_reason": { "anyOf": [ { "type": "integer" }, { "type": "string" }, { "type": "null" } ], "title": "Stop Reason" } }, "additionalProperties": false, "type": "object", "required": [ "index", "delta" ], "title": "ChatCompletionResponseStreamChoice" }, "ChatCompletionStreamResponse": { "properties": { "id": { "type": "string", "title": "Id" }, "object": { "type": "string", "enum": [ "chat.completion.chunk" ], "const": "chat.completion.chunk", "title": "Object", "default": "chat.completion.chunk" }, "created": { "type": "integer", "title": "Created" }, "model": { "type": "string", "title": "Model" }, "choices": { "items": { "$ref": "#/components/schemas/ChatCompletionResponseStreamChoice" }, "type": "array", "title": "Choices" }, "usage": { "anyOf": [ { "$ref": "#/components/schemas/UsageInfo" }, { "type": "null" } ] } }, "additionalProperties": false, "type": "object", "required": [ "model", "choices" ], "title": "ChatCompletionStreamResponse" }, "ChatMessage": { "properties": { "role": { "type": "string", "title": "Role" }, "content": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Content" } }, "additionalProperties": false, "type": "object", "required": [ "role" ], "title": "ChatMessage" }, "DeltaMessage": { "properties": { "role": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Role" }, "content": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Content" } }, "additionalProperties": false, "type": "object", "title": "DeltaMessage" }, "ErrorResponse": { "properties": { "object": { "type": "string", "title": "Object", "default": "error" }, "message": { "type": "string", "title": "Message" }, "type": { "type": "string", "title": "Type" }, "param": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Param" }, "code": { "type": "integer", "title": "Code" } }, "additionalProperties": false, "type": "object", "required": [ "message", "type", "code" ], "title": "ErrorResponse" }, "AudioURL": { "properties": { "url": { "type": "string", "title": "Url" } }, "type": "object", "required": [ "url" ], "title": "AudioURL" }, "InputAudio": { "properties": { "data": { "type": "string", "title": "Data" }, "format": { "type": "string", "enum": [ "wav", "mp3" ], "title": "Format" } }, "type": "object", "required": [ "data", "format" ], "title": "InputAudio" }, "ImageURL": { "properties": { "url": { "type": "string", "title": "Url" } }, "type": "object", "required": [ "url" ], "title": "ImageURL" }, "VideoURL": { "properties": { "url": { "type": "string", "title": "Url" } }, "type": "object", "required": [ "url" ], "title": "VideoURL" }, "NIMLLMChatCompletionMessage": { "properties": { "role": { "allOf": [ { "$ref": "#/components/schemas/Role" } ], "description": "The role of the message's author." }, "content": { "anyOf": [ { "type": "string" }, { "items": { "anyOf": [ { "$ref": "#/components/schemas/NIMLLMChatCompletionContentPartAudio" }, { "$ref": "#/components/schemas/NIMLLMChatCompletionContentPartInputAudio" }, { "$ref": "#/components/schemas/NIMVLMChatCompletionContentPartImage" }, { "$ref": "#/components/schemas/NIMVLMChatCompletionContentPartVideo" }, { "$ref": "#/components/schemas/NIMVLMChatCompletionContentPartText" } ] }, "type": "array" } ], "title": "Content", "description": "The contents of the message.\n
To pass media (only with role=`user`):\n
- Use content parts. Text is passed with `type`=`text`.\n
- Images are passed with `type`=`image_url`; set `image_url.url` to an image URL or a base64 data URI like `data:image/{format};base64,{base64encodedimage}`.\n
- Videos use `type`=`video_url`; audios use `type`=`audio_url` or `type`=`input_audio` when supported by the model schema.\n
For `system` and `assistant` roles, the object list format is not supported.\n" } }, "type": "object", "required": [ "role", "content" ], "title": "NIMLLMChatCompletionMessage" }, "NIMLLMChatCompletionRequest": { "properties": { "messages": { "items": { "$ref": "#/components/schemas/NIMLLMChatCompletionMessage" }, "type": "array", "minItems": 1, "title": "Messages", "description": "A list of messages comprising the conversation so far." }, "model": { "type": "string", "title": "Model", "description": "The model to use.", "default": "nvidia/nemotron-3-nano-omni-30b-a3b-reasoning" }, "max_tokens": { "anyOf": [ { "type": "integer", "maximum": 65536, "minimum": 1 }, { "type": "null" } ], "title": "Max Tokens", "description": "The maximum number of tokens that can be generated.", "default": 65536 }, "reasoning_budget": { "type": "integer", "minimum": -1, "maximum": 32768, "title": "Reasoning Budget", "description": "Maximum number of tokens the model is allowed to use for internal reasoning before the reasoning trace is forced to end. Use `-1` to disable budget enforcement. This can also be provided via `chat_template_kwargs.reasoning_budget` for backwards compatibility.", "default": 16384 }, "seed": { "anyOf": [ { "type": "integer", "maximum": 9223372036854776000, "minimum": -9223372036854776000 }, { "type": "null" } ], "title": "Seed", "description": "Changing the seed will produce a different response with similar characteristics. Fixing the seed will reproduce the same results if all other parameters are also kept constant." }, "stream": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Stream", "description": "If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]`", "default": false }, "temperature": { "anyOf": [ { "type": "number", "maximum": 2, "minimum": 0 }, { "type": "null" } ], "title": "Temperature", "description": "What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.", "default": 0.6 }, "top_p": { "anyOf": [ { "type": "number", "maximum": 1, "exclusiveMinimum": 0 }, { "type": "null" } ], "title": "Top P", "description": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or `temperature` but not both.", "default": 0.95 } }, "additionalProperties": false, "type": "object", "required": [ "messages", "model" ], "title": "NIMLLMChatCompletionRequest" }, "NIMLLMChatCompletionContentPartAudio": { "properties": { "audio_url": { "$ref": "#/components/schemas/AudioURL" }, "type": { "type": "string", "enum": [ "audio_url" ], "const": "audio_url", "title": "Type" } }, "type": "object", "required": [ "audio_url", "type" ], "title": "NIMLLMChatCompletionContentPartAudio" }, "NIMLLMChatCompletionContentPartInputAudio": { "properties": { "input_audio": { "$ref": "#/components/schemas/InputAudio" }, "type": { "type": "string", "const": "input_audio", "title": "Type" } }, "type": "object", "required": [ "input_audio", "type" ], "title": "NIMLLMChatCompletionContentPartInputAudio" }, "NIMVLMChatCompletionContentPartImage": { "properties": { "image_url": { "$ref": "#/components/schemas/ImageURL" }, "type": { "type": "string", "enum": [ "image_url" ], "const": "image_url", "title": "Type" } }, "type": "object", "required": [ "image_url", "type" ], "title": "NIMVLMChatCompletionContentPartImage" }, "NIMVLMChatCompletionContentPartVideo": { "additionalProperties": false, "properties": { "video_url": { "description": "Video url", "allOf": [ { "$ref": "#/components/schemas/VideoURL" } ] }, "start_offset": { "description": "video offset position", "anyOf": [ { "type": "number", "maximum": 9223372036854776000, "minimum": 0 }, { "type": "null" } ], "title": "Start Offset" }, "duration": { "description": "video inference duration", "anyOf": [ { "type": "number", "maximum": 9223372036854776000, "minimum": 0 }, { "type": "null" } ], "title": "Duration" }, "type": { "type": "string", "description": "The type of the content part.", "enum": [ "video_url" ], "const": "video_url", "title": "Type" } }, "required": [ "type", "video_url" ], "type": "object", "title": "NIMVLMChatCompletionContentPartVideo" }, "NIMVLMChatCompletionContentPartText": { "properties": { "text": { "type": "string", "title": "Text" }, "type": { "type": "string", "enum": [ "text" ], "const": "text", "title": "Type" } }, "type": "object", "required": [ "text", "type" ], "title": "NIMVLMChatCompletionContentPartText" }, "Role": { "type": "string", "enum": [ "system", "assistant", "user" ], "title": "Role" }, "UsageInfo": { "properties": { "prompt_tokens": { "type": "integer", "title": "Prompt Tokens", "default": 0 }, "total_tokens": { "type": "integer", "title": "Total Tokens", "default": 0 }, "completion_tokens": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "title": "Completion Tokens", "default": 0 } }, "additionalProperties": false, "type": "object", "title": "UsageInfo" } } }, "x-readme": { "explorer-enabled": true, "proxy-enabled": true } } ```