A curated gallery of CLI demos sourced from `voyageai-cli`, with exact commands, prerequisites, and direct links to the original tape files.
13 published demos
22 learning paths
Source of truth: `voyageai-cli/docs/demos` and `docs/demos/tapes`
A lightweight starting set focused on developer education and reproducibility.
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.
Run a local CLI workflow with Ollama generation and local embeddings, without a Voyage API key.
VAI command
vai embed "Local inference keeps retrieval private, fast, and API-key free." --local --dimensions 256
Show Under the Hood
Prerequisites
Ollama is installed and running locally.
Build a tiny Atlas-backed RAG chat flow using local nano embeddings and Ollama for generation.
VAI command
vai chat --db "$DEMO_DB" --collection "$DEMO_COLLECTION" --local --llm-provider ollama --llm-model "$OLLAMA_MODEL" --llm-base-url http://localhost:11434 --no-history --no-stream
Show Under the Hood
Prerequisites
Ollama is installed and running locally.
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.
Run the full workflow in one command: create sample docs, chunk them, embed them, store them in Atlas, and auto-create the vector index.
VAI command
vai pipeline /tmp/vai-demo-docs/ --db vai_demo --collection knowledge --create-index
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.
The complete MongoDB Developer Day vector search workshop reimagined as a VAI CLI workflow. Use it as a backup during the Vector Search: Beginner to Pro lab or afterward to reinforce concepts: set up local MongoDB, install the CLI, generate embeddings with voyage-4-nano, create a vector index, and run semantic search — end to end from the command line.
VAI command
vai query "A man wearing a golden crown" --db mongodb_genai_devday_vs --collection books --model voyage-4-lite
Show Under the Hood
Prerequisites
Docker installed (for local MongoDB Atlas) or access to a MongoDB Atlas cluster.
The complete MongoDB Developer Day RAG lab reimagined as a VAI CLI workflow. Use it as a backup during the Build RAG Applications using MongoDB lab or afterward to reinforce concepts: ingest documentation chunks, create a vector index, and run RAG chat — end to end from the command line.
VAI command
vai chat --db mongodb_genai_devday_rag --collection knowledge_base
Show Under the Hood
Prerequisites
Docker installed (for local MongoDB Atlas) or access to a MongoDB Atlas cluster.
13 results matching the current filters.
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.
Run a local CLI workflow with Ollama generation and local embeddings, without a Voyage API key.
VAI command
vai embed "Local inference keeps retrieval private, fast, and API-key free." --local --dimensions 256
Show Under the Hood
Prerequisites
Ollama is installed and running locally.
Build a tiny Atlas-backed RAG chat flow using local nano embeddings and Ollama for generation.
VAI command
vai chat --db "$DEMO_DB" --collection "$DEMO_COLLECTION" --local --llm-provider ollama --llm-model "$OLLAMA_MODEL" --llm-base-url http://localhost:11434 --no-history --no-stream
Show Under the Hood
Prerequisites
Ollama is installed and running locally.
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.
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.
Compare fixed, sentence, and markdown chunking on the same sample document before any embedding or storage layer is introduced.
VAI command
vai chunk /tmp/sample.md --strategy markdown
Show Under the Hood
Prerequisites
The `vai` CLI is installed locally. No API key is required for chunking-only workflows.
Run the full workflow in one command: create sample docs, chunk them, embed them, store them in Atlas, and auto-create the vector index.
VAI command
vai pipeline /tmp/vai-demo-docs/ --db vai_demo --collection knowledge --create-index
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.
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.
Survey the Voyage model lineup, explain benchmark context, and then measure embedding latency on your own hardware before choosing a production model.
VAI command
vai benchmark embed --input 'What is the best way to index a large document corpus?'
Show Under the Hood
Prerequisites
A valid VOYAGE_API_KEY is set in the environment.
The complete MongoDB Developer Day vector search workshop reimagined as a VAI CLI workflow. Use it as a backup during the Vector Search: Beginner to Pro lab or afterward to reinforce concepts: set up local MongoDB, install the CLI, generate embeddings with voyage-4-nano, create a vector index, and run semantic search — end to end from the command line.
VAI command
vai query "A man wearing a golden crown" --db mongodb_genai_devday_vs --collection books --model voyage-4-lite
Show Under the Hood
Prerequisites
Docker installed (for local MongoDB Atlas) or access to a MongoDB Atlas cluster.
The complete MongoDB Developer Day RAG lab reimagined as a VAI CLI workflow. Use it as a backup during the Build RAG Applications using MongoDB lab or afterward to reinforce concepts: ingest documentation chunks, create a vector index, and run RAG chat — end to end from the command line.
VAI command
vai chat --db mongodb_genai_devday_rag --collection knowledge_base
Show Under the Hood
Prerequisites
Docker installed (for local MongoDB Atlas) or access to a MongoDB Atlas cluster.
