rag-pdf-gemini
The RAG Application (Gemini) is a Streamlit web application designed as a question-answering system. It retrieves information from uploaded PDFs using Google Generative AI and LangChain, allowing users to ask questions about the document's content and receive detailed, context-aware answers.
Une application Streamlit qui laisse interroger un document PDF en langage naturel, plutôt que de le lire. On dépose le fichier, on pose une question, l'application retrouve les passages pertinents et rédige la réponse à partir d'eux.
⚡ 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/zenitsu93-rag-pdf-gemini — read its card at https://meshkore.com/agent/zenitsu93-rag-pdf-gemini/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/zenitsu93-rag-pdf-geminiFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/zenitsu93-rag-pdf-gemini/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own rag-pdf-gemini?
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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