Bring Vector Database
to LangChain

Turn your OpenSearch cluster into an AI-native vector database. Create k-NN indexes, upsert embeddings, run similarity searches, and inspect index configurations — all through natural conversation with your AI agent.

What you can do

• Vector Search — Execute k-Nearest Neighbors queries against any k-NN index with custom top-K limits and dense float vectors
• Index Management — List all cluster indexes with health status and document counts, or inspect a specific index's vector dimension, engine config, and distance metric
• Create Index — Provision new k-NN indexes optimized for cosine similarity with configurable vector dimensions (384, 768, 1536, etc.)
• Document Operations — Upsert vector documents with metadata, or delete documents from the embedding space by ID

How it works

1. Subscribe to this server
2. Enter your OpenSearch Host, Username, and Password
3. Start managing your vector store from Claude, Cursor, or any MCP-compatible client

Who is this for?

• ML engineers — test similarity queries against production embeddings without writing curl commands
• RAG developers — index and retrieve context documents for retrieval-augmented generation pipelines
• Data teams — inspect index health, document counts, and vector configurations through conversation instead of Kibana dashboards