--- updatedAt: 2026-08-06T09:59:31.000Z --- # Creates an embedding vector from the input text. # OpenAPI definition ```json { "openapi": "3.1.0", "info": { "title": "NVIDIA NIM API for nvidia/llama-nemotron-embed-vl-1b-v2", "description": "The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/nvidia-llama-nemotron-embed-vl-1b-v2 for more details.", "version": "1.0.0", "termsOfService": "https://nvidia.com/legal/terms-of-use", "contact": { "name": "NVIDIA Support", "url": "https://help.nvidia.com/" } }, "servers": [ { "url": "https://integrate.api.nvidia.com/v1" } ], "paths": { "/embeddings": { "post": { "tags": [ "Embeddings" ], "summary": "Creates an embedding vector from the input text.", "operationId": "create_embedding", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "input": { "description": "Input text to embed. Max length is 4096 tokens.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "minLength": 1, "maxLength": 4096, "title": "Input" }, "model": { "type": "string", "description": "ID of the embedding model.", "example": "nvidia/llama-nemotron-embed-vl-1b-v2", "default": "nvidia/llama-nemotron-embed-vl-1b-v2", "title": "Model" }, "input_type": { "type": "string", "enum": [ "passage", "query" ], "description": "nvidia/llama-nemotron-embed-vl-1b-v2 operates in `passage` or `query` mode, and thus require the `input_type` parameter. `passage` is used when generating embeddings during indexing. `query` is used when generating embeddings during querying. It is very important to use the correct `input_type`. Failure to do so will result in large drops in retrieval accuracy.", "title": "Input Type" }, "encoding_format": { "type": "string", "description": "The format to return the embeddings in.", "enum": [ "float", "base64" ], "default": "float", "title": "Encoding Format" }, "truncate": { "type": "string", "description": "Specifies how inputs longer than the maximum token length of the model are handled. Passing `START` discards the start of the input. `END` discards the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model. If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.", "enum": [ "NONE", "START", "END" ], "default": "NONE", "title": "Truncate" }, "user": { "type": "string", "description": "Not implemented, but provided for API compliance. This field is ignored.", "title": "User" } }, "required": [ "input", "model" ] } } } }, "x-nvai-meta": { "name": "Create Text Embedding", "description": "Generates an embedding vector from the provided text\nusing a specified model. The embedding can be returned\nin either float array or base64-encoded format.\n", "path": "create", "examples": [ { "name": "Embedding vector for text input", "shouldShowImage": false, "requestJson": "{\n \"input\": \"What is the civil caseload in South Dakota courts?\",\n \"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2\",\n \"input_type\": \"query\",\n \"image\": \"https://assets.ngc.nvidia.com/products/api-catalog/nemo-retriever/embedding/court-sizing-metrics.png\",\n \"image_name\": \"court-sizing-metrics.png\",\n \"encoding_format\": \"float\",\n \"truncate\": \"NONE\"\n}\n", "responseJson": "{\n \"object\": \"list\",\n \"data\": [\n {\n \"index\": 0,\n \"embedding\": [\n 0.01436614990234375,\n 0.010711669921875,\n 0.0031642913818359375,\n 0.003742218017578125,\n 0.032867431640625,\n 0.037872314453125,\n 0.01058197021484375,\n -0.0033111572265625,\n -0.0220489501953125,\n 0.01207733154296875,\n -0.0114898681640625,\n -0.0004584789276123047,\n -0.0032806396484375,\n 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-0.01140594482421875,\n 0.005756378173828125,\n -0.01258087158203125,\n 0.0045166015625,\n 0.00543975830078125,\n -0.011199951171875,\n 0.057373046875,\n 0.00604248046875,\n 0.0292510986328125,\n 0.018829345703125,\n -0.0201568603515625,\n 0.00867462158203125,\n -0.0181884765625,\n -0.0124053955078125,\n -0.02032470703125,\n 0.0181121826171875,\n -0.004314422607421875,\n 0.0050048828125,\n 0.006778717041015625,\n -0.0022830963134765625,\n 0.0255889892578125,\n 0.00522613525390625,\n -0.005153656005859375,\n -0.0112152099609375,\n -0.011260986328125,\n -0.0300445556640625,\n -0.004993438720703125,\n 0.0208282470703125,\n 0.0140533447265625,\n 0.00926971435546875,\n -0.005420684814453125,\n 0.0002532005310058594,\n -0.050445556640625,\n 0.0000514984130859375,\n 0.0141448974609375,\n 0.0009489059448242188,\n 0.004199981689453125\n ],\n \"object\": \"embedding\"\n }\n ],\n \"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2\",\n \"usage\": {\n \"prompt_tokens\": 1802,\n \"total_tokens\": 1802\n }\n}\n" } ], "templates": [ { "title": "Synchronous requests", "requestEjs": { "curl": "<% if (request.input_type === 'query') { %>\ncurl -X POST https://integrate.api.nvidia.com/v1/embeddings \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $NVIDIA_API_KEY\" \\\n -d '{\n \"input\": [\"<%- request.input.replaceAll('\"', '\\\\\"').replaceAll(\"'\", \"'\\\"'\\\"'\") %>\"],\n \"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2\",\n \"modality\": [\"text\"],\n \"input_type\": \"<%- request.input_type %>\",\n \"encoding_format\": \"<%- request.encoding_format %>\",\n \"truncate\": \"<%- request.truncate %>\"\n }'\n<% } else { %>\nimage_source=\"https://assets.ngc.nvidia.com/products/api-catalog/nemo-retriever/embedding/court-sizing-metrics.png\"\nif [[ $image_source == http* ]]; then\n base64_image=$(curl -s \"${image_source}\" | base64 -w 0)\nelse\n base64_image=$(base64 -w 0 < \"${image_source}\")\nfi\n\njson_payload='{\n \"input\": [\"<% if (request.input) { %><%- request.input.replaceAll('\"', '\\\\\"').replaceAll(\"'\", \"'\\\"'\\\"'\") %> <% } %>data:image/png;base64,'\"${base64_image}\"'\"],\n \"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2\",\n \"modality\": [<% if (request.input) { %>\"text_image\"<% } else { %>\"image\"<% } %>],\n \"input_type\": \"<%- request.input_type %>\",\n \"encoding_format\": \"<%- request.encoding_format %>\",\n \"truncate\": \"<%- request.truncate %>\"\n}'\n\necho \"${json_payload}\" | \\\n curl -X POST https://integrate.api.nvidia.com/v1/embeddings \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $NVIDIA_API_KEY\" \\\n -d @-\n<% } %>\n", "python": "<% if (request.input_type === 'query') { %>\nfrom openai import OpenAI\n\nclient = OpenAI(\n api_key=\"$NVIDIA_API_KEY\",\n base_url=\"https://integrate.api.nvidia.com/v1\"\n)\n\nresponse = client.embeddings.create(\n input=[<%- JSON.stringify(request.input) %>],\n model=\"nvidia/llama-nemotron-embed-vl-1b-v2\",\n encoding_format=\"<%- request.encoding_format %>\",\n extra_body={\"modality\": [\"text\"], \"input_type\": \"<%- request.input_type %>\", \"truncate\": \"<%- request.truncate %>\"}\n)\n\nprint(response.data[0].embedding)\n<% } else { %>\nimport base64\nimport requests\nfrom openai import OpenAI\n\nimage_source = \"https://assets.ngc.nvidia.com/products/api-catalog/nemo-retriever/embedding/court-sizing-metrics.png\"\n\nif image_source.startswith(('http://', 'https://')):\n response = requests.get(image_source)\n image_b64 = base64.b64encode(response.content).decode()\nelse:\n with open(image_source, \"rb\") as image_file:\n image_b64 = base64.b64encode(image_file.read()).decode()\n\nclient = OpenAI(\n api_key=\"$NVIDIA_API_KEY\",\n base_url=\"https://integrate.api.nvidia.com/v1\"\n)\n\nresponse = client.embeddings.create(\n input=[<% if (request.input) { %>f\"<%- request.input.replaceAll('\"', '\\\\\"') %> data:image/png;base64,{image_b64}\"<% } else { %>f\"data:image/png;base64,{image_b64}\"<% } %>],\n model=\"nvidia/llama-nemotron-embed-vl-1b-v2\",\n encoding_format=\"<%- request.encoding_format %>\",\n extra_body={\"modality\": [<% if (request.input) { %>\"text_image\"<% } else { %>\"image\"<% } %>], \"input_type\": \"<%- request.input_type %>\", \"truncate\": \"<%- request.truncate %>\"}\n)\n\nprint(response.data[0].embedding)\n<% } %>\n", "langchain": "<% if (request.input_type === 'query') { %>\nfrom langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\n\nclient = NVIDIAEmbeddings(\n model=\"nvidia/llama-nemotron-embed-vl-1b-v2\",\n api_key=\"$NVIDIA_API_KEY\",\n truncate=\"<%- request.truncate %>\",\n )\n\nembedding = client.embed_query(<%- JSON.stringify(request.input) %>)\nprint(embedding)\n<% } else { %>\nimport base64\nimport requests\nfrom langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\n\nimage_source = \"https://assets.ngc.nvidia.com/products/api-catalog/nemo-retriever/embedding/court-sizing-metrics.png\"\n\nif image_source.startswith(('http://', 'https://')):\n response = requests.get(image_source)\n image_b64 = base64.b64encode(response.content).decode()\nelse:\n with open(image_source, \"rb\") as image_file:\n image_b64 = base64.b64encode(image_file.read()).decode()\n\nclient = NVIDIAEmbeddings(\n model=\"nvidia/llama-nemotron-embed-vl-1b-v2\",\n api_key=\"$NVIDIA_API_KEY\",\n truncate=\"<%- request.truncate %>\",\n )\n\nimage_input = <% if (request.input) { %>f\"<%- request.input.replaceAll('\"', '\\\\\"') %> data:image/png;base64,{image_b64}\"<% } else { %>f\"data:image/png;base64,{image_b64}\"<% } %>\n\nembedding = client.embed_documents([image_input])\nprint(embedding)\n<% } %>\n" }, "response": "{\n \"object\": \"list\",\n \"data\": [\n {\n \"object\": \"embedding\",\n \"embedding\": [\n -0.021759033203125,\n 0.028717041015625,\n -0.00675201416015625,\n 0.0290374755859375,\n 0.03314208984375,\n -0.00557708740234375,\n -0.0633544921875,\n -0.0167694091796875,\n -0.0277252197265625,\n 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