feat: MongoDB voyage-multimodal-3.5 launch

[Sam-Deciga]

REQUIRED: Add a summary of your PR here, typically including why the change is needed and what was changed. Include any design alternatives for discussion purposes.

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MongoDB voyage-multimodal-3.5 launch on Jan 15, 2026
b/475223167

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1. If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:

• Use the notebook template as a starting point.
• Follow the style and grammar rules outlined in the above notebook template.
• Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
• Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
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• The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.

2. If you are opening a PR for Community Notebooks under the notebooks/community folder:

• This notebook has been added to the CODEOWNERS file under the Community Notebooks section, pointing to the author or the author's team.
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• Passes all the required formatting and linting checks. You can locally test with these instructions.

[gemini-code-assist]

Summary of Changes

Hello @Sam-Deciga, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new official notebook to the Vertex AI samples, providing a comprehensive guide for deploying and interacting with the Voyage Multimodal 3.5 embedding model. The notebook enables users to generate embeddings from diverse data types, including text, images, and video, and demonstrates how to perform cross-modal semantic searches. It also covers advanced configuration options for optimizing embedding generation based on specific use cases and resource constraints.

Highlights

• New Multimodal Embedding Model Notebook: A new Jupyter notebook has been added to demonstrate the deployment and usage of the Voyage Multimodal 3.5 embedding model on Vertex AI.
• Comprehensive Embedding Generation: The notebook illustrates how to generate embeddings for various data types including text, images (via URL or base64), and video (via URL or base64), as well as combined multimodal inputs.
• Cross-Modal Semantic Search Capabilities: Examples are provided for using the generated embeddings to perform cross-modal semantic similarity tasks, enabling search across different modalities.
• Advanced Configuration Options: The notebook details advanced parameters such as input_type (query vs. document), truncation behavior, output_encoding (float vs. base64), output_dimension (256, 512, 1024, 2048), and output_dtype (float, int8, uint8, binary, ubinary) for optimized embedding generation.

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[gemini-code-assist]

Code Review

This pull request introduces a new notebook for the voyage-multimodal-3.5 model. The notebook is well-structured and provides a comprehensive guide to using the model's features. I've identified a few issues, including a critical syntax error that will prevent a code cell from running, and some minor bugs in print statements. I've provided suggestions to fix these issues.

[gericdong]

Update the copyright year to 2026

[gericdong]

Please address the code review comments

[Sam-Deciga]

Copyright year was changed to 2026, and code reviews were addressed by the partner