Start from first principles: generate a Voyage embedding, inspect its shape, and compare full-size versus Matryoshka-reduced vectors.
• A valid VOYAGE_API_KEY is set in the environment.
See the exact VAI command, the matching Voyage AI layer, and the MongoDB query shape behind the demo.
vai embed 'MongoDB Atlas makes vector search production-ready'
The demo starts with the smallest useful mental model: one sentence in, one embedding out. The follow-up dimensions example shows how you can cut storage with Matryoshka-style reduction while preserving the semantic task.
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The full walkthrough is included here so anyone can replay the demo exactly as published.
More shareable workflows from the same VAI demo library.
Show why input type matters by embedding the same sentence as both a document and a query, then validating the semantic overlap with similarity.
VAI command
vai embed 'Vector search finds semantically similar content' --input-type query
Show Under the Hood
Prerequisites
A valid VOYAGE_API_KEY is set in the environment.
Walk through the core vai embedding commands: model discovery, embedding generation, explainers, and similarity.
VAI command
vai embed "What is MongoDB Atlas?"
Show Under the Hood
Prerequisites
A valid VOYAGE_API_KEY is set in the environment.
