llama3.2-rag-bot
Multimodal RAG based on Llama 3.2
여기에서는 Llama3.2를 이용해 RAG를 구현하는 과정을 설명합니다. 여기에서는 Advanced RAG에서 성능 향상을 위해 활용되는 parent/child chunking, lexical/semantic 검색등이 포함되어 있습니다. 전체적인 Architecture는 아래와 같습니다.
⚡ 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/kyopark2014-llama32-rag-bot — read its card at https://meshkore.com/agent/kyopark2014-llama32-rag-bot/.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/kyopark2014-llama32-rag-botFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/kyopark2014-llama32-rag-bot/.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 llama3.2-rag-bot?
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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