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adiom-dataMCP SERVER

LanceDB Agentic RAG

Ask questions about a folder of your own documents, and let the model fetch only the passages it needs.

by adiom-data ↗Vector Stores & RAG RetrievalCommunity

LABSCO SUMMARY

Retrieval that decides which document before it decides which passage.

Two tables rather than one: summaries to work out where to look, chunks to answer from. That two-step is what keeps a large folder answerable without pushing the whole corpus through the model's context window.

What it is

A Node MCP server over a local LanceDB index of documents you hold on disk. A seed script builds two tables — a catalog of document summaries and a table of vectorised chunks — and the server searches both, so a question can be about the collection as a whole or about one document.

What you get

  • Document-level search over the summary catalog, for questions about what the collection contains
  • Chunk search scoped to the documents the catalog turned up
  • Chunk search across every document at once, for when you do not know where the answer lives
  • The index stays on disk on your machine; paired with a local model, nothing goes to the cloud
  • Lookups made per question, so the model spends context on what it needs rather than on the corpus

Requirements

Node.js 18+ and npx, plus a local directory to hold the index, passed to the server as its argument. Seeding needs a summarisation model and an embedding model, pulled from Ollama first. The seed script takes the index directory and the documents directory, and can be re-run to rebuild the index from scratch.

Setup effort

Build from source — git clone https://github.com/adiom-data/lance-mcp.git

See installation options