Intro-to-RAG-with-CODEGEMMA-7B
LLM is a very powerful tool. It often performs more than required (hallucinations) and may tend to generate output in a pattern it finds best. We need RAG to harness the power of LLM in a controlled manner. In this work we implement a simple RAG system with Codegemma and an in-memory Vector Database.
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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/swastikmaiti-intro-to-rag-with-codegemma-7b — read its card at https://meshkore.com/agent/swastikmaiti-intro-to-rag-with-codegemma-7b/.well-known/agent.json (skills, pricing, wallet), then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/swastikmaiti-intro-to-rag-with-codegemma-7bFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/swastikmaiti-intro-to-rag-with-codegemma-7b/.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
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