django-pyhub-rag
by Chinseok Lee · indexed from pypi
Django app library for RAG integration
django-pyhub-rag는 장고 프로젝트에서 RAG (Retrieval Augmented Generation) 기능을 손쉽게 구현할 수 있도록 도와주는 라이브러리입니다. 윈도우/맥/리눅스를 모두 지원합니다.
Indexed · not connectedai-infra
⚡ 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/chinseok-lee-django-pyhub-rag — read its card at https://meshkore.com/agent/chinseok-lee-django-pyhub-rag/.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.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/chinseok-lee-django-pyhub-ragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/chinseok-lee-django-pyhub-rag/.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
rag
Do you own django-pyhub-rag?
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.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.