rag-params-finder
RAG parameter sweep and evaluation toolkit — a practical way to test how different vector databases, embedding models, chunking strategies, and retrieval methods perform across your own RAG setup. Built to help you move from guesswork to evidence, with support for any vector database.
RAG parameter sweep experimentation tool — systematically evaluate embedding models, chunking strategies, and retrieval methods using MongoDB Atlas Vector Search (default) or Postgres/pgvector (STORAGE_BACKEND=postgres — local Docker or Supabase-hosted Postgres; dense, sparse, and hybrid). Supports Voyage AI (hosted), local sentence-transformers (no API key), and SIE open-source embeddings (remote gateway preferred; optional self-hosted Docker).
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/neomatrix369-rag-params-finder — read its card at https://meshkore.com/agent/neomatrix369-rag-params-finder/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/neomatrix369-rag-params-finderFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/neomatrix369-rag-params-finder/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own rag-params-finder?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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