ragrqs
static region:us
La tecnica RAG (Retrieval-Augmented Generation) è un approccio consolidato nel campo del question answering e della generazione di testo, che combina il recupero di informazioni pertinenti da fonti di dati con la generazione di testo basata su queste informazioni. Qui viene proposta una implementazione che introduce una variazione a questo paradigma. L'implementazione si basa su una sequenza di prompt appositamente progettati per guidare un modello di linguaggio generativo attraverso le diverse fasi della tecnica RAG.
⚡ 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/gmaterni-ragrqs — read its card at https://meshkore.com/agent/gmaterni-ragrqs/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/gmaterni-ragrqsFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/gmaterni-ragrqs/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own ragrqs?
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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