Show why input type matters by embedding the same sentence as both a document and a query, then validating the semantic overlap with similarity.
• 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 'Vector search finds semantically similar content' --input-type query
The tape uses the same sentence twice so the only changing variable is intent. That makes it easy to see why input_type is a retrieval choice, not just a syntactic flag.
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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.
Start from first principles: generate a Voyage embedding, inspect its shape, and compare full-size versus Matryoshka-reduced vectors.
VAI command
vai embed 'MongoDB Atlas makes vector search production-ready'
Show Under the Hood
Prerequisites
A valid VOYAGE_API_KEY is set in the environment.
Walk through the classic retrieval stack: embed the query, run Atlas vector search, rerank the candidates, then compare the result to a vector-only pass.
VAI command
vai query 'how does vector search work?' --db vai_demo --collection knowledge --model voyage-4-lite
Show Under the Hood
Prerequisites
A valid VOYAGE_API_KEY is set in the environment.
