feat: MongoDB voyage-4 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.

--- YOUR PR SUMMARY GOES HERE --- MongoDB voyage-4 model launch on Jan 15, 2026 b/475223740

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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.
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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 integrates a new official notebook into the repository, designed to guide users through the process of leveraging the Voyage 4 family of embedding models on Vertex AI. The notebook emphasizes the models' innovative capabilities, including a unified embedding space for flexible model mixing, and techniques for optimizing embedding size and precision to suit diverse application requirements. It serves as a comprehensive resource for deploying, utilizing, and understanding the advanced features of these powerful embedding models.

Highlights

• New Voyage 4 Embedding Models Notebook: Introduced a new official Jupyter notebook (voyage-4.ipynb) to demonstrate the deployment and usage of the Voyage 4 family of embedding models on Google Cloud's Vertex AI.
• Shared Embedding Space: The notebook highlights the industry-first shared embedding space of Voyage 4 models, allowing interchangeability between different model sizes (large, standard, lite) for optimal cost and performance.
• Advanced Embedding Features: Showcases advanced features such as Matryoshka Representation Learning (MRL) for variable-dimension embeddings (256, 512, 1024, 2048) and Quantization-Aware Training (QAT) for optimized output data types (float, int8, uint8, binary, ubinary).
• Practical Deployment and Usage: Provides practical examples for deploying a Voyage 4 model to a Vertex AI endpoint, generating embeddings, performing semantic similarity calculations, and exploring the impact of various parameters like input_type, output_dimension, and output_dtype.

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[gemini-code-assist]

Code Review

This pull request introduces a new notebook for the Voyage 4 embedding models. The notebook is well-structured and provides a good overview of the model's features.

My review includes a few suggestions to improve code maintainability and consistency:

• Updating an image URL to match the style of other notebooks.
• Refactoring two code cells to remove hardcoded values and magic numbers, making the code more robust and easier to read.

[gericdong]

Update the copyright year for 2026.

[Sam-Deciga]

Copyright year updated to 2026 and changes applied by the partner