Survey the Voyage model lineup, explain benchmark context, and then measure embedding latency on your own hardware before choosing a production model.
• 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 benchmark embed --input 'What is the best way to index a large document corpus?'
The catalog and explainer parts of the tape build context, but the benchmark command is where selection becomes operational. It lets you test model latency against your own prompt shape before committing.
Share or copy this demo
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LinkedIn opens the share dialog and copies the prepared text so you can paste it in quickly.
The full walkthrough is included here so anyone can replay the demo exactly as published.
More shareable workflows from the same VAI demo library.
Validate that Voyage 4 models share an embedding space, then connect that result to asymmetric retrieval and concrete cost savings.
VAI command
vai benchmark space
Show Under the Hood
Prerequisites
A valid VOYAGE_API_KEY is set in the environment.
Rerank intentionally messy candidate documents against a query, then compare the full reranker to the lite version to show the latency-precision tradeoff.
VAI command
vai rerank 'how do I connect to MongoDB Atlas?' --documents 'Use the connection string from your Atlas dashboard' 'Python is a popular language' 'Atlas supports vectorSearch aggregation' 'Copy your URI and pass it to MongoClient' 'The weather in San Francisco is mild'
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.
