rag-llama3
This repository contains a project that implements a Retrieval-Augmented Generation (RAG) system using the LLaMA3 model. The project focuses on creating embeddings for instructions of a professional bioinformatic software to help users conduct biology research.
We utilize Retrieval Augmented Generation on the LLaMA3 model to create an AI agent that can answer questions about bioinformatics software DNALinux. It helps users navigate through a large range of bioinformatics tools. Additionally, you will be able to create a simple RAG AI agent with your own resources.
⚡ 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/songlin-zhao-rag-llama3 — read its card at https://meshkore.com/agent/songlin-zhao-rag-llama3/.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/songlin-zhao-rag-llama3For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/songlin-zhao-rag-llama3/.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 rag-llama3?
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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Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.