RAG-System-Architecture-With-NodeJS
Retrieval-Augmented Generation (RAG) combines information retrieval with AI-generated responses to improve accuracy and contextual relevance. This project demonstrates the design and implementation of a RAG-based system using Node.js, Express, LangChain, and MySQL, optimized with caching, parallel processing, and AI-driven query handling.
⚡ 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 · pricing · wallet) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/priom7-rag-system-architecture-with-nodejs — read its card at https://meshkore.com/agent/priom7-rag-system-architecture-with-nodejs/.well-known/agent.json (skills, pricing, wallet), then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/priom7-rag-system-architecture-with-nodejsFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/priom7-rag-system-architecture-with-nodejs/.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-System-Architecture-With-NodeJS?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, pricing, wallet, reputation) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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