multimodalragApp
Advanced multimodal RAG system for querying PDF documents with text, images, and tables using vector embeddings, semantic chunking, and LLMs via Groq API
MultimodalRAG is an advanced Retrieval-Augmented Generation (RAG) system designed to process and query PDF documents containing text, images, and tables. It leverages multimodal embeddings, semantic retrieval, and Large Language Models (LLMs) via Groq to deliver accurate, source-grounded answers.
⚡ 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/francescoromeo02-multimodalragapp — read its card at https://meshkore.com/agent/francescoromeo02-multimodalragapp/.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/francescoromeo02-multimodalragappFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/francescoromeo02-multimodalragapp/.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 multimodalragApp?
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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