Quick starts · Library catalog · Deployment
Start with a language tutorial, then choose a system capability. Each example has its own dependencies and runtime requirements.
| Track | Tutorial | What to verify |
|---|---|---|
npm ruvector |
Node.js | Write records, exit, and retrieve in another process |
Python ruvector |
Python installation and SDK | Save a collection, load it, and search |
Rust ruvector-core |
Rust | Insert, reopen the same store, and recover the record |
| Claude Code MCP | Setup and project instructions | Connect and complete a permitted read |
- Local ONNX embeddings
- ONNX embeddings in WASM
- Typed local decisions
- Browser search with React
- Browser search with vanilla JavaScript
- Edge examples
- Graph queries and relationships
- Knowledge graph embeddings
- MinCut graph diagnostics
- MRAgent memory reconstruction
- SONA adaptation
- Contrastive training
- RuVLLM runtime
- Agent to agent swarm
- REFRAG document pipeline
- RVF artifacts
- Google Cloud deployment examples
- Python Salesforce Agentforce
Use its documented setup directory, dependencies, and feature flags. Fixed or random vectors demonstrate API mechanics; semantic search requires a consistent embedding model. Compare learning changes against a frozen baseline on separate evaluation data. A successful tutorial run does not establish a production latency or accuracy guarantee.
Research examples · Agentic version control · Graph CLI guide