rag_gpt_chatbot
This is ChatBot Django Base Backend App. It uses RAG GPT Api's
This project involves the creation of an API server that harnesses Retrieval Augmented Generation (RAG) with GPT to answer user queries within a chatbot application. The ultimate objective is to develop a robust API capable of utilizing a knowledge base to provide well-informed responses.
⚡ 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/muhammadb1094-raggptchatbot — read its card at https://meshkore.com/agent/muhammadb1094-raggptchatbot/.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/muhammadb1094-raggptchatbotFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/muhammadb1094-raggptchatbot/.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 rag_gpt_chatbot?
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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