Voyage AI embeddings + MongoDB Atlas Vector SearchCLI: 33 Commands
Embed, search, rerank, ingest, evaluate, benchmark, and more. One tool for the entire RAG pipeline, from chunking documents to querying with reranking.
MCP Server: 22 Tools
Drop vai into Claude Desktop, Cursor, or any MCP-compatible editor: retrieval, multimodal embeddings, workspace + code-index search, ingest, cost estimates, and workflow authoring.
Agentic Workflows
Define multi-step RAG pipelines as portable JSON files. Search multiple collections, merge results, filter by relevance, and summarize with LLMs.
From zero to semantic search in 5 minutes
Everything you need for RAG
Voyage AI Embeddings
State-of-the-art embedding models including voyage-3-large, domain-specific models for code, finance, law, and multilingual content.
MongoDB Atlas Vector Search
Store embeddings and run vector searches directly against your Atlas cluster. Automatic index management included.
Two-Stage Retrieval
Combine fast vector search with Voyage AI reranking for dramatically better result quality.
Chat with Your Docs
RAG-powered chat using Anthropic, OpenAI, or local Ollama models. Conversation history and agent mode included.
Evaluation and Benchmarks
Test retrieval quality with custom test sets, compare configurations, and benchmark embedding performance.
30 Educational Topics
Built-in explanations of embeddings, vector search, RAG, MoE architecture, quantization, and more.
vai is a community project by Michael Lynn. It is not an official MongoDB or Voyage AI product. Use of Voyage AI APIs and MongoDB Atlas requires accounts with those services.