In the era of vector search and semantic similarity, returning highly relevant results is only half the battle. When search results are too similar, users must wade through redundant information to find diverse perspectives. This talk introduces Maximum Marginal Relevance (MMR) implementation in OpenSearch, a powerful technique that optimally balances result relevance with diversity.

Key takeaways will include:
- Implementing MMR reranking with OpenSearch's vector search
- Optimizing performance for large-scale deployments
- Measuring and tuning diversity metrics
- Real-world applications and success patterns