RAG-LLaMA
A clean and simple implementation of Retrieval Augmented Generation (RAG) to enhanced LLaMA chat model to answer questions from a private knowledge base. We use Tesla user manuals to build the knowledge base, and use open-source embedding and Cross-Encoders reranking models from Sentence Transformers in this project.
⚡ 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/michaelnny-rag-llama — read its card at https://meshkore.com/agent/michaelnny-rag-llama/.well-known/agent.json (skills, pricing, wallet), then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/michaelnny-rag-llamaFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/michaelnny-rag-llama/.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-LLaMA?
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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