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In this skill badge, you’ll learn what vector embeddings are, how they’re created, and why they’re essential for powering modern AI applications and semantic search. You’ll explore how to store and query embeddings efficiently in MongoDB, and how to leverage MongoDB auto-embedding feature to generate and maintain embeddings in your database . Through hands-on labs, you’ll generate embeddings and apply Voyage AI models for both vector search and reranking, building a two-step search pipeline that boosts the relevance and context-awareness of your results. You’ll also examine key design choices—like model selection, chunking and context length, voyage-context-3, and embedding dimensionality—to balance search quality, latency, and cost for your workloads.
