--- title: "nemotron-3.5-embed-1b" publisher: "nvidia" type: "endpoint" updated: "2026-10-20T23:06:34.068Z" description: "1B multimodal embedding model for text and document-image retrieval in RAG applications." canonical: "https://build.nvidia.com/nvidia/nemotron-3_5-embed-1b" --- # Nemotron 3.5 Embed 1B Overview ## Description: **Nemotron-3.5-Embed-1B** is a versatile embedding model trained by NVIDIA and optimized for multimodal and multilingual question-answering retrieval. The model can embed document pages in the form of image, text, or combined image–text inputs. Documents can be retrieved given a user query in text form. The model supports page images containing text, tables, charts, and infographics. For text retrieval, the model was evaluated across 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepali, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, and Vietnamese. For image retrieval, it was evaluated across 18 languages: Arabic, Bengali, Chinese, English, Persian, Finnish, French, German, Hindi, Indonesian, Japanese, Korean, Russian, Spanish, Swahili, Telugu, Thai, and Yoruba. The model may support additional languages beyond these evaluation sets. _This model is ready for commercial or non-commercial use._ Nemotron 3.5 Embed 1B is released in **BF16 and NVFP4** variants. This combined Model Card covers both variants. NVIDIA API Catalog provides one hosted Preview API experience using the **BF16** variant. **Nemotron-3.5-Embed-1B-NVFP4** is the quantized version of the [Nemotron-3.5-Embed-1B-BF16](https://huggingface.co/nvidia/Nemotron-3.5-Embed-1B-BF16) model, developed for multimodal and multilingual question-answering retrieval. The model can embed document pages in the form of image, text, or combined image–text inputs. Documents can be retrieved given a user query in text form. The model supports page images containing text, tables, charts, and infographics. The model is quantized with NVIDIA Model Optimizer, using nvidia-modelopt v0.47.0. ## License/Terms of Use: **Governing Hosted-Service Terms:** Use of this trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf)
**Source Model License:** Use of the model is governed by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1).
**Additional Information:** The [Ministral-3-3B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512-BF16) and [Ministral-3-8B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-BF16) base models are governed by the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). ## Deployment Geography: Global ## Use Case: The **Nemotron-3.5-Embed-1B-BF16** is suitable for users who want to build a multimodal question-and-answer application over a large corpus, leveraging the latest dense retrieval technology. The input of the model is a text or a document image, and the output is a fixed-size embedding vector. The embedding model is a bi-encoder that supports context in textual format (e.g. the query or the OCR text of a page or a section of a document) or the image of a document page. Typically, the embedding model is used first to embed (vectorize) the whole corpus (document images or text chunks), and embeddings are stored in a vector database associated to its raw content (image or text). Then at inference time, the embedding model is used to embed the query. The model generates dense vector embeddings from text or image inputs, enabling multimodal retrieval, semantic search, and (agentic) RAG workflows. Generally **Nemotron-3.5-Embed-1B-BF16** and **Nemotron-3.5-Embed-1B-NVFP4** models share the embedding space and can be used interchangeably, but we recommend validating retrieval quality on a representative sample before switching the models. ## Release Date: **Build.NVIDIA.com:** 10/20/2026 via [model page](https://build.nvidia.com/nvidia/nemotron-3_5-embed-1b)
**Hugging Face (BF16):** 10/20/2026 via [BF16 Model Card](https://huggingface.co/nvidia/Nemotron-3.5-Embed-1B-BF16)
**Hugging Face (NVFP4):** 10/20/2026 via [NVFP4 Model Card](https://huggingface.co/nvidia/Nemotron-3.5-Embed-1B-NVFP4) ## Model Architecture: **Architecture Type:** Transformer
**Number of Model Parameters:** 1.55B ### BF16 **Network Architecture:** `Nemotron-3.5-Embed-1B-BF16` consists of a Pixtral vision encoder initialized from [mistralai/Ministral-3-3B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512-BF16) and a language model initialized from [nvidia/Nemotron-3-Embed-1B-BF16](https://huggingface.co/nvidia/Nemotron-3-Embed-1B-BF16).
The `Nemotron-3.5-Embed-1B-BF16` model was distilled from the [Nemotron-3.5-Embed-8B-BF16](https://huggingface.co/nvidia/Nemotron-3.5-Embed-8B-BF16) embedding teacher using combined cosine distance (COS) and mean squared error (MSE) losses on multilingual text and image retrieval data. Its language-model component was initialized from [Nemotron-3-Embed-1B-BF16](https://huggingface.co/nvidia/Nemotron-3-Embed-1B-BF16) and kept frozen during distillation. Consequently, `Nemotron-3.5-Embed-1B-BF16` shares the same text embedding space as `Nemotron-3-Embed-1B-BF16` while adding image retrieval capabilities. The vision encoder and multimodal projection were fine-tuned on image and combined image-text datasets. ### NVFP4 **Network Architecture:** The `Nemotron-3.5-Embed-1B-NVFP4` is the quantized version of the `Nemotron-3.5-Embed-1B-BF16`, which is a transformer-based multimodal embedding model distilled from a fine-tuned `Nemotron-3.5-Embed-8B-BF16` teacher model. For distribution, the NVFP4 checkpoint was converted to the compressed-tensors format. NVFP4 quantization is applied to eligible linear layers in both the language model and the vision encoder. The final embedding vector is obtained by applying average pooling to the transformer’s token-level representations. It encodes each input (image, text, or image + text) into a dense embedding vector of dimension 2048. #### Post-Training Quantization and Quantization-Aware Distillation This model is a post-training quantized variant of `Nemotron-3.5-Embed-1B-BF16`. Quantization was applied to the weights and activations of linear layers only, targeting the NVFP4 data type for efficient inference. Quantization-Aware Distillation (QAD) was applied primarily to recover accuracy for long input sequences. ## Input: **Input Type(s):** Text, Image
**Input Format(s):** Text: String; Image: Red, Green, Blue (RGB)
**Input Parameters:** Text: One-Dimensional (1D), Image: Two-Dimensional (2D)
**Other Properties Related to Input:** The model supports image and/or text inputs. For queries, the model accepts text input only. For documents, it accepts an image, text, or a combination of image + text. The default image `longest_edge` is 1,120 pixels, and it can be increased up to 1,540 pixels. The source models’ max context length is 32,768 tokens.
**Hosted API Input Handling:** Image inputs use `input_type="passage"`; queries are text-only. `truncate="END"` removes text from the end, while `truncate="NONE"` returns an error for over-limit inputs. An image block that alone exceeds the limit is rejected. See the [API reference](https://docs.api.nvidia.com/nim/reference/nvidia-nemotron-3_5-embed-1b). ## Output: **Output Type(s):** Embeddings
**Output Format:** Vector
**Output Parameters:** One-Dimensional (1D) per input
**Other Properties Related to Output:** Token-level hidden states are mean-pooled and then L2-normalized to produce one 2048-dimensional embedding vector for each input. The model also supports dynamic embedding sizes by truncating the vector from the end (e.g., retaining the first 1024 or 512 dimensions). The truncated embeddings remain effective for retrieval, provided they are L2-normalized after truncation. Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. ## Software Integration: **Runtime:** NVIDIA hosted Preview API
**API Endpoint and Request/Response Format:** `POST https://integrate.api.nvidia.com/v1/embeddings` with JSON requests and embedding responses; model ID `nvidia/nemotron-3.5-embed-1b`. See the [API reference](https://docs.api.nvidia.com/nim/reference/nvidia-nemotron-3_5-embed-1b). | Variant | Runtime Engine(s) | Supported Hardware Microarchitecture Compatibility | Supported Operating System(s) | | --- | --- | --- | --- | | BF16 | Transformers, Sentence Transformers, vLLM | NVIDIA Ampere, NVIDIA Blackwell, NVIDIA Hopper | Linux | | NVFP4 | vLLM | NVIDIA Blackwell | Linux | The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment. ## Model Version(s): - **Nemotron-3.5-Embed-1B-BF16:** The variant served by the NVIDIA API Catalog Preview API. See the [BF16 Model Card](https://huggingface.co/nvidia/Nemotron-3.5-Embed-1B-BF16). - **Nemotron-3.5-Embed-1B-NVFP4:** The NVFP4 quantized derivative, covered here with its own architecture, quantization, training, runtime, and evaluation details. See the [NVFP4 Model Card](https://huggingface.co/nvidia/Nemotron-3.5-Embed-1B-NVFP4). Shared input/output properties and the four Model Card++ subcards apply to both variants. Variant-specific disclosures are identified explicitly. ## Training, Testing, and Evaluation Datasets: ### BF16 Training Dataset **Data Modality:** - Image - Text **Image Training Data Size:** 2.3M samples containing images, with or without accompanying text
**Text Training Data Size:** 8.5M+ text-only data samples
**Data Collection Method by dataset:** Automated, Manually-Labeled, Synthetic
**Labeling Method by dataset:** Automated, Manually-Labeled, Synthetic
**Properties:** Training was conducted using publicly available, commercially permissible datasets and synthetically generated datasets. #### Public Datasets: The language model of **Nemotron-3.5-Embed-1B-BF16** was trained with text-only datasets that have been listed in [Nemotron-3-Embed-1B-BF16's](https://huggingface.co/nvidia/Nemotron-3-Embed-1B-BF16#dataset-overview) Model Card. In addition to these datasets we have used the following publicly available datasets to train the vision encoder and multimodal projector with image-only and image + text inputs. | Dataset name | Reference | | --- | --- | | ArxivQA | [https://huggingface.co/datasets/MMInstruction/ArxivQA](https://huggingface.co/datasets/MMInstruction/ArxivQA) | | ColPali-PDF | [https://huggingface.co/datasets/vidore/colpali_train_set](https://huggingface.co/datasets/vidore/colpali_train_set) | | InfoVQA | [https://www.docvqa.org/datasets/infographicvqa](https://www.docvqa.org/datasets/infographicvqa) | | docmatix-ir | [https://huggingface.co/datasets/Tevatron/docmatix-ir](https://huggingface.co/datasets/Tevatron/docmatix-ir) | | Docmatix | [https://huggingface.co/datasets/HuggingFaceM4/Docmatix](https://huggingface.co/datasets/HuggingFaceM4/Docmatix) | | wiki-ss-nq | [https://huggingface.co/datasets/Tevatron/wiki-ss-nq](https://huggingface.co/datasets/Tevatron/wiki-ss-nq) | Synthetic datasets were created either by generating queries from seed documents or by generating complete question–answer pairs through LLM-based prompting using the models listed below.

LLMs used to generate synthetic datasets
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4
Qwen/Qwen3-Next-80B-A3B-Instruct Qwen/Qwen3-235B-A22B Qwen/Qwen3.5-397B-A17B Qwen/Qwen3.5-35B-A3B Qwen/Qwen3.6-27B Qwen/Qwen3.6-35B-A3B Qwen/Qwen3.8-27B
google/gemma-4-31B-it
mistralai/Pixtral-12B-2409
openai/gpt-oss-120b openai/gpt-oss-20b

Seed Datasets
Dataset	Reference
FinePdfs	https://huggingface.co/datasets/HuggingFaceFW/finepdfs
CentralActs	https://zenodo.org/records/5088102
BRIGHT	https://huggingface.co/datasets/xlangai/BRIGHT
MultiHiertt	https://github.com/psunlpgroup/MultiHiertt
Digital Corpora	https://digitalcorpora.org/corpora/file-corpora/cc-main-2021-31-pdf-untruncated/
Vdr-multilingual	https://huggingface.co/datasets/llamaindex/vdr-multilingual-train

### NVFP4 Quantization and QAD Dataset **Data Modality:** - Image - Text **Image Training Data Size:** 49k samples
**Text Training Data Size:** 49k samples
**Data Collection Method by dataset:** Automated, Manually-Labeled, Synthetic
**Labeling Method by dataset:** Automated, Manually-Labeled, Synthetic
**Properties:** This checkpoint is an NVFP4 post-training-quantized derivative of [nvidia/Nemotron-3.5-Embed-1B-BF16](https://huggingface.co/nvidia/Nemotron-3.5-Embed-1B-BF16). NVFP4 quantization and Quantization-Aware Distillation (QAD) used a 49k-sample subset of the training data, selected to cover typical cases and tail cases, and containing both images and text. Of these samples, initial post-training quantization calibration used 384 samples: 128 images, 128 short texts, and 128 long texts. Initial QAD of the vision encoder used 5,120 randomly sampled images. Later text and image+text QAD stages used the full 49k-sample subset, with 5% held out for validation. `Nemotron-3.5-Embed-1B-BF16's` training was conducted using publicly available, commercially permissible datasets and synthetically generated datasets. For more detail about the text and image datasets used for training please refer to the [BF16 model card](https://huggingface.co/nvidia/Nemotron-3.5-Embed-1B-BF16). ### Testing Dataset **Data Collection Method by dataset:** Not Applicable
**Labeling Method by dataset:** Not Applicable
**Properties:** Not Applicable. Model quality was assessed using the evaluation benchmark datasets described in the Evaluation Dataset subsection. ### BF16 Evaluation Dataset **Benchmark**: - [ViDoRe V3](https://huggingface.co/blog/QuentinJG/introducing-vidore-v3) - [MIRACL-VISION ](https://huggingface.co/datasets/nvidia/miracl-vision)
**Benchmark Score:** NDCG@10
The **Nemotron-3.5-Embed-1B-BF16** and **Nemotron-3.5-Embed-8B-BF16** were evaluated with the models' default image `longest_edge` of 1,120 pixels. [Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) and [Qwen3-VL-Embedding-8B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) were evaluated with `max_pixels` set to 1,843,200.

Detailed BF16 Benchmark Comparison

Model	ViDoRe V3			MIRACL-VISION
Model	image	text	image + text	image
Qwen3-VL-Embedding-2B	52.89	48.31	51.97	56.19
Nemotron-3.5-Embed-1B-BF16	58.02	57.80	58.82	66.56
Qwen3-VL-Embedding-8B	58.29	53.69	57.03	65.87
Nemotron-3.5-Embed-8B-BF16	60.39	60.65	61.47	70.15

Model	Precision	ViDoRe V3
Model	Precision	image	text	image + text
Nemotron-3.5-Embed-1B-BF16	BF16	58.02	57.80	58.82
Nemotron-3.5-Embed-1B-NVFP4	NVFP4	57.82	57.53	58.71

**Data Collection Method by dataset:** Automated, Manually-Labeled, Synthetic
**Labeling Method by dataset:** Automated, Manually-Labeled, Synthetic
**Properties:** `ViDoRe V3` is a visual document retrieval benchmark covering 8 public and 2 private datasets across professional domains. ## Inference: **Served Variant:** BF16
**Acceleration Engine:** Other (Photon Engine; native CUDA backend)
**Test Hardware:** NVIDIA Ampere (A100, A10G), NVIDIA Blackwell (GB10, GB200, RTX-PRO-6000-BSE), NVIDIA Hopper (H100, H100-NVL), NVIDIA Lovelace (L40S) ### Source Model Inference Requirements | Variant | Acceleration Engine(s) | Hardware Requirements (GPU Architecture, Model) | | --- | --- | --- | | BF16 | Transformers, PyTorch, vLLM | NVIDIA Ampere (A100 80GB), NVIDIA Blackwell (GB200 186GB), NVIDIA Hopper (H100 80GB) | | NVFP4 | vLLM | NVIDIA Blackwell (GB200 186GB) | ## Ethical Considerations: NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included. For more detailed information on ethical considerations for both variants, see the Model Card++ documents: - [Bias](bias.md) - [Explainability](explainability.md) - [Privacy](privacy.md) - [Safety & Security](safety.md) Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). ## Capabilities - **Function Calling:** Not supported ## Bias # Nemotron 3.5 Embed 1B — Bias Applies to BF16 and NVFP4. | Field | Response | | ----- | ----- | | Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None | | Measures taken to mitigate against unwanted bias: | None | | Bias Metric (If Measured): | Not Applicable | ## Explainability # Nemotron 3.5 Embed 1B — Explainability Applies to BF16 and NVFP4; source-license details are identified by variant. | Field | Response | | ----- | ----- | | Intended Task/Domain: | Document and query embedding for question and answer retrieval. | | Model Type: | Transformer encoder | | Intended Users: | Generative AI creators working with conversational AI models - users who want to build a multilingual and multimodal question and answer application over a large document corpus, leveraging the latest dense retrieval technologies. | | Output: | Array of float numbers (Dense Vector Representation for the input text or image) | | Describe how the model works: | Model transforms the tokenized input into a dense vector representation. | | List the groups (or group characteristics) for which this was tested to produce comparable outcomes: | Not Applicable | | Technical Limitations & Mitigation: | The model’s max sequence length is 32768. Therefore, the longer inputs should be truncated. | | Verified to have met prescribed NVIDIA quality standards: | Yes | | Performance Metrics: | Accuracy, Latency and Throughput | | Potential Known Risks: | This model does not always guarantee to retrieve the correct document(s) for a given query. | | Licensing: | Hosted-service terms: Use of this trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). **BF16 source model:** Use of the model is governed by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1). Additional Information: The [Ministral-3-3B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512-BF16) and [Ministral-3-8B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-BF16) base models are governed by the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
**NVFP4 source model:** Use of the model is governed by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1). Additional Information: The [Ministral-3-8B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-BF16) base model is governed by the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). | ## Privacy # Nemotron 3.5 Embed 1B — Privacy Applies to BF16 and NVFP4. The datasets used to train this model were reviewed before release. Please review [NVIDIA’s Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) for more information. ## Safety & Security # Nemotron 3.5 Embed 1B — Safety & Security Applies to BF16 and NVFP4; source-license details are identified by variant. | Field | Response | | ----- | ----- | | Model Application Field(s): | Document Embedding for Retrieval. User queries can be text and documents can be text, document page images, charts, tables, and infographics. | | Describe the life critical impact (if present). | Not Applicable | | Use Case Restrictions: | Hosted-service terms: Use of this trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). **BF16 source model:** Use of the model is governed by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1). Additional Information: The [Ministral-3-3B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512-BF16) and [Ministral-3-8B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-BF16) base models are governed by the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
**NVFP4 source model:** Use of the model is governed by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1). Additional Information: The [Ministral-3-8B-Instruct-2512-BF16](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-BF16) base model is governed by the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). | | Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. | ## Prototype ```bash image_source="https://assets.ngc.nvidia.com/products/api-catalog/nemotron-rerank-vl/example1.jpg" if [[ $image_source == http* ]]; then base64_image=$(curl -s "${image_source}" | base64 -w 0) else base64_image=$(base64 -w 0 < "${image_source}") fi json_payload='{ "input": ["data:image/png;base64,'"${base64_image}"'"], "model": "nvidia/nemotron-3.5-embed-1b", "modality": ["image"], "input_type": "", "encoding_format": "float", "truncate": "END" }' echo "${json_payload}" | \ curl -X POST https://integrate.api.nvidia.com/v1/embeddings \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $NVIDIA_API_KEY" \ -d @- ``` ```python import base64 import requests from openai import OpenAI image_source = "https://assets.ngc.nvidia.com/products/api-catalog/nemotron-rerank-vl/example1.jpg" if image_source.startswith(('http://', 'https://')): response = requests.get(image_source) image_b64 = base64.b64encode(response.content).decode() else: with open(image_source, "rb") as image_file: image_b64 = base64.b64encode(image_file.read()).decode() client = OpenAI( api_key="$NVIDIA_API_KEY", base_url="https://integrate.api.nvidia.com/v1" ) response = client.embeddings.create( input=[f"data:image/png;base64,{image_b64}"], model="nvidia/nemotron-3.5-embed-1b", encoding_format="float", extra_body={"modality": ["image"], "input_type": "", "truncate": "END"} ) print(response.data[0].embedding) ``` ```python import base64 import requests from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings image_source = "https://assets.ngc.nvidia.com/products/api-catalog/nemotron-rerank-vl/example1.jpg" if image_source.startswith(('http://', 'https://')): response = requests.get(image_source) image_b64 = base64.b64encode(response.content).decode() else: with open(image_source, "rb") as image_file: image_b64 = base64.b64encode(image_file.read()).decode() client = NVIDIAEmbeddings( model="nvidia/nemotron-3.5-embed-1b", api_key="$NVIDIA_API_KEY", truncate="END", ) image_input = f"data:image/png;base64,{image_b64}" embedding = client.embed_documents([image_input]) print(embedding) ```