Adding Maximal Marginal Relevance (MMR) for searching

Is there an existing issue?

• I have searched existing issues

Use case

Imagine you search for “AI in healthcare” in a news app.

A normal nearestNeighborsF32 search might return the top 10 most relevant articles — but 8 of them may be about chatbots for hospitals, basically saying the same thing.

With Maximal Marginal Relevance (MMR), the system instead returns:

• An article about chatbots in hospitals
• One about AI in medical imaging
• One about drug discovery
• One about patient data analysis

This way, you still get relevant results for your query, but also diverse perspectives, rather than numerous near-duplicates.

Proposed solution

• Support Maximal Marginal Relevance (MMR): a method for retrieving documents that balances similarity to the query with diversity among the selected items. It is calculated as follows:

• Alternative solution: expose an API for calculating the similarity between two vector embeddings, allowing developers to implement MMR within their own applications.

Additional context

• I’d be happy to contribute if you can point me to where I should start.

Start by locating the Dart API for nearestNeighborsF32 and reviewing how vector search results are exposed. Compare whether the project should provide MMR directly or expose embedding-similarity calculation for application code. Done means the chosen API is defined, documented, and covered by appropriate search behavior tests.