Local_RAG_LLM
I built a chatbot based on LLaMA 3 and stable diffusion. You can ask him anything, upload PDF files and question them and even generate images !
The rapid advancement of artificial intelligence (AI) has led to the development of various chatbots that can converse with humans. However, most existing chatbots are limited in their capabilities, only able to answer questions or provide information within a specific domain or cost money to use. In this article, we will show you how to build an AI chatbot that not only answers questions but also generates images using the stable diffusion model and provides a RAG module to question you documents.
⚡ 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/batoutou-localragllm — read its card at https://meshkore.com/agent/batoutou-localragllm/.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/batoutou-localragllmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/batoutou-localragllm/.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 Local_RAG_LLM?
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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