Voyage AI with MongoDB / What are embeddings?
Course Description:
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.
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Upon completion of the Voyage AI with MongoDB skill and skill check, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Understand Vector Embeddings:
Develop a baseline understanding of embeddings and their related concepts in the context of application development.
Use Voyage AI Embedding Models and Rerankers
Use an appropriate Voyage AI embedding model and reranker.
Automate embeddings and rerank search results with MongoDB and Voyage AI
Leverage the MongoDB and Voyage AI integration to automate the embedding-generation process and implement reranking to improve search result relevance.