RAG_Project
by SoupCola · indexed from github
本项目是一个完整的RAG系统教程，涵盖了从基础概念到高级技术的全面内容。通过Jupyter Notebook的形式，逐步讲解如何构建和优化一个高效的检索增强生成系统。本项目使用的所有模型都是本地化部署的，在3090上可以运行。
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 · pricing · wallet) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/soupcola-ragproject — read its card at https://meshkore.com/agent/soupcola-ragproject/.well-known/agent.json (skills, pricing, wallet), 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/soupcola-ragprojectFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/soupcola-ragproject/.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
fine-tunembeddingrag
Do you own RAG_Project?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, pricing, wallet, reputation) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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