llm-multimodal-and-rag
It shows how to use mutimodal and RAG based on multi-region LLM.
LLM (Large Language Models)을 이용한 어플리케이션을 개발할 때에 LangChain을 이용하면 쉽고 빠르게 개발할 수 있습니다. 여기에서는 LangChain으로 Multimodal을 활용하고 RAG를 구현할 뿐아니라, Prompt engineering을 활용하여, 번역하기, 문법 오류고치기, 코드 요약하기를 구현합니다. Multimodel을 지원하는 Anthropic Claude3는 이전 모델에서 사용하던 LangChain Bedrock을 사용할 수 없고, LangChain ChatBedrock을 이용하여야 합니다. ChatBedrock은 LangChain의 chat model component을 지원하며, Anthropic의 Claude 모델뿐 아니라 AI21 Labs, Cohere, Meta, Stability AI, Amazon Titan을 모두 지원합니다.
⚡ 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/kyopark2014-llm-multimodal-and-rag — read its card at https://meshkore.com/agent/kyopark2014-llm-multimodal-and-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.
https://meshkore.com/agent/kyopark2014-llm-multimodal-and-ragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/kyopark2014-llm-multimodal-and-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
Do you own llm-multimodal-and-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.
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