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Built with Llama.\n","showUnavailableBanner":false,"cta":{"text":"Apply to Self-Host","url":"https://www.nvidia.com/en-us/ai/nim-notifyme/","nim_available_override_url":"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/llama-3.2-nv-embedqa-1b-v2"},"playground":{"type":"custom","input":{"items":[{"key":"input","type":"text-area"},{"key":"input_type","type":"select"},{"key":"encoding_format","type":"select"},{"key":"truncate","type":"select"}]},"parameters":{"omitProperties":["input_type","encoding_format","truncate"]},"output":{"items":[{"key":"data[0].embedding","type":"code","language":"javascript","formatAsJson":true}]},"requestBody":"{ \"input\": [\"<%- request.input %>\"], \"model\": \"nvidia/llama-3.2-nv-embedqa-1b-v2\", \"input_type\": \"<%- request.input_type %>\", \"encoding_format\": \"<%- request.encoding_format %>\", \"truncate\": \"<%- request.truncate %>\" }"},"deploy":[{"label":"Linux with Docker","filename":"linux.md","contents":"$7b"},{"label":"Windows on RTX AI PCs (Beta)","filename":"wsl2.md","contents":"$7c"}]},"artifactName":"llama-3_2-nv-embedqa-1b-v2"},"config":{"name":"llama-3_2-nv-embedqa-1b-v2","type":"model"}},{"endpoint":{"artifact":{"name":"nvclip","displayName":"nvclip","publisher":"nvidia","shortDescription":"NV-CLIP is a multimodal embeddings model for image and text.","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/nvclip.jpg","labels":["Computer vision","NVIDIA NIM","Run on rtx","multimodal embeddings","text and image"],"attributes":[{"key":"AVAILABLE","value":"true"},{"key":"PREVIEW","value":"false"}],"artifactType":"ENDPOINT","bias":"| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | Not Applicable |","canGuestDownload":true,"createdDate":"2024-06-13T20:35:52.208Z","description":"$7d","explainability":"$7e","isPublic":true,"isReadOnly":true,"orgName":"qc69jvmznzxy","privacy":"$7f","safetyAndSecurity":"| Field | Response |\n| -- | -- |\n| Model Application(s): | Image Search |\n| Describe the life-critical application (if present). | None: Not within Operational Design Domain |\n| Use Case Restrictions: | Abide by [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\"](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. |","updatedDate":"2025-04-11T18:04:11.709Z"},"requestStatus":{"statusCode":"SUCCESS","requestId":"77bfc5a2-e003-4b3c-9f16-4af378186ce8"}},"spec":{"openAPISpec":{"openapi":"3.1.0","info":{"title":"NVIDIA NIM API for nvidia/nvclip","description":"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim 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 AI Foundation Models Community License","url":"https://docs.nvidia.com/ai-foundation-models-community-license.pdf"}},"servers":[{"url":"https://integrate.api.nvidia.com/v1"}],"tags":[{"name":"NVIDIA NV-CLIP API","description":"Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms."}],"paths":{"/embeddings":{"post":{"tags":["Embeddings"],"summary":"Creates an embedding vector representing the input text or image.","description":"Invokes inference using the embedding parameters. If uploading large images, this POST should be used in conjunction with the NVCF API which allows for the upload of large assets. \nYou can find details on how to use NVCF Asset APIs here: https://docs.api.nvidia.com/cloud-functions/reference/createasset.","operationId":"createEmbedding","parameters":[{"in":"header","name":"NVCF-INPUT-ASSET-REFERENCES","schema":{"type":"string","maxLength":370,"format":"uuid"},"required":false,"description":"String of asset IDs separated by commas. Data is uploaded to AWS S3 using NVCF Asset APIs and associated with these asset IDs.If the size of an image is more than 180KB, it needs to be uploaded to a presigned S3 URL bucket. The presigned URL allows for secure and temporary access to the S3 bucket for uploading the image. Once the asset is requested, an asset ID is generated for it. Please include this asset ID in this header and to use the uploaded image in a prompt, you need to refer to it using the following format: `

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This field is ignored.","pattern":"^.*$","maxLength":204800,"example":"user123"}},"type":"object","required":["input","model"],"title":"EmbeddingsRequest"},"EmbeddingsResponse":{"type":"object","properties":{"object":{"description":"The object type, which is always `list`.","enum":["list"],"title":"Object","type":"string"},"data":{"items":{"$ref":"#/components/schemas/Embedding"},"type":"array","title":"Data","description":"The list of embeddings generated by the model.","maxItems":128,"examples":[[{"embedding":[0.1,0.1,0.1],"index":0,"object":"embedding"},{"embedding":[0.1,0.1,0.1],"index":1,"object":"embedding"}]]},"model":{"type":"string","description":"Model used to generate embeddings.","example":"nvidia/nvclip","title":"Model","const":"nvidia/nvclip"},"usage":{"allOf":[{"$ref":"#/components/schemas/Usage"}],"description":"Usage statistics for the embeddings request.","examples":[{"num_images":1,"prompt_tokens":80,"total_tokens":80}]}},"required":["object","data","usage","model"],"title":"EmbeddingsResponse"},"Embedding":{"type":"object","description":"Represents an embedding vector returned by embedding endpoint.","properties":{"object":{"description":"The object type, which is always `embedding`.","enum":["embedding"],"title":"Object","type":"string"},"index":{"type":"integer","format":"int32","title":"Index","description":"The index of the embedding in the list of embeddings.","minimum":0,"maximum":127},"embedding":{"oneOf":[{"type":"array","description":"The embedding vector as a list of floats, The length of the vector depends on the model.\n","items":[{"type":"number"}],"maxItems":1024},{"type":"string","description":"The embedding vector as base64 string. The length of the vector depends on the model.\n","format":"^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)$"}]},"title":"Embedding"},"required":["index","object","embedding"],"title":"Embeddings"},"Usage":{"properties":{"num_images":{"type":"integer","format":"int32","title":"Num Images","minimum":0,"maximum":64,"description":"Number of images passed."},"prompt_tokens":{"type":"integer","format":"int32","title":"Prompt Tokens","description":"Number of tokens in the prompt.","minimum":77,"maximum":4928},"total_tokens":{"type":"integer","format":"int32","title":"Total Tokens","description":"Total number of tokens used in the request.","minimum":77,"maximum":4928}},"type":"object","required":["num_images","prompt_tokens","total_tokens"],"title":"Usage"},"Errors":{"properties":{"type":{"type":"string","format":"^.{1, 128}$","maxLength":128,"description":"Error type"},"title":{"type":"string","format":"^.{1, 128}$","maxLength":128,"description":"Error 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